diff --git a/data/convert_mcnp_70.py b/data/convert_mcnp_70.py
new file mode 100755
index 000000000..e68bd848e
--- /dev/null
+++ b/data/convert_mcnp_70.py
@@ -0,0 +1,88 @@
+#!/usr/bin/env python
+
+from __future__ import print_function
+from argparse import ArgumentParser
+from collections import defaultdict
+import glob
+import os
+
+import openmc.data
+
+
+# Get path to MCNP data
+parser = ArgumentParser()
+parser.add_argument('-d', '--destination', default='mcnp_endfb70',
+                    help='Directory to create new library in')
+parser.add_argument('mcnpdata', help='Directory containing endf70[a-k] and endf70sab')
+args = parser.parse_args()
+assert os.path.isdir(args.mcnpdata)
+
+# Get a list of all neutron ACE files
+endf70 = glob.glob(os.path.join(args.mcnpdata, 'endf70[a-k]'))
+
+# Create output directory if it doesn't exist
+if not os.path.isdir(args.destination):
+    os.mkdir(args.destination)
+
+library = openmc.data.DataLibrary()
+
+for path in sorted(endf70):
+    print('Loading data from {}...'.format(path))
+    lib = openmc.data.ace.Library(path)
+
+    # Group together tables for the same nuclide
+    tables = defaultdict(list)
+    for table in lib.tables:
+        zaid, xs = table.name.split('.')
+        tables[zaid].append(table)
+
+    for zaid, tables in sorted(tables.items()):
+        # Convert first temperature for the table
+        print('Converting: ' + tables[0].name)
+        data = openmc.data.IncidentNeutron.from_ace(tables[0], 'mcnp')
+
+        # For each higher temperature, add cross sections to the existing table
+        for table in tables[1:]:
+            print('Adding: ' + table.name)
+            data.add_temperature_from_ace(table, 'mcnp')
+
+        # Export HDF5 file
+        h5_file = os.path.join(args.destination, data.name + '.h5')
+        print('Writing {}...'.format(h5_file))
+        data.export_to_hdf5(h5_file, 'w')
+
+        # Register with library
+        library.register_file(h5_file)
+
+# Handle S(a,b) tables
+endf70sab = os.path.join(args.mcnpdata, 'endf70sab')
+if os.path.exists(endf70sab):
+    lib = openmc.data.ace.Library(endf70sab)
+
+    # Group together tables for the same nuclide
+    tables = defaultdict(list)
+    for table in lib.tables:
+        name, xs = table.name.split('.')
+        tables[name].append(table)
+
+    for zaid, tables in sorted(tables.items()):
+        # Convert first temperature for the table
+        print('Converting: ' + tables[0].name)
+        data = openmc.data.ThermalScattering.from_ace(tables[0])
+
+        # For each higher temperature, add cross sections to the existing table
+        for table in tables[1:]:
+            print('Adding: ' + table.name)
+            data.add_temperature_from_ace(table)
+
+        # Export HDF5 file
+        h5_file = os.path.join(args.destination, data.name + '.h5')
+        print('Writing {}...'.format(h5_file))
+        data.export_to_hdf5(h5_file, 'w')
+
+        # Register with library
+        library.register_file(h5_file)
+
+# Write cross_sections.xml
+libpath = os.path.join(args.destination, 'cross_sections.xml')
+library.export_to_xml(libpath)
diff --git a/data/convert_mcnp_71.py b/data/convert_mcnp_71.py
new file mode 100755
index 000000000..7e7fd923f
--- /dev/null
+++ b/data/convert_mcnp_71.py
@@ -0,0 +1,77 @@
+#!/usr/bin/env python
+
+from __future__ import print_function
+from argparse import ArgumentParser
+from collections import defaultdict
+import glob
+import os
+
+import openmc.data
+
+
+# Get path to MCNP data
+parser = ArgumentParser()
+parser.add_argument('-d', '--destination', default='mcnp_endfb71',
+                    help='Directory to create new library in')
+parser.add_argument('-f', '--fission_energy_release',
+                    help='HDF5 file containing fission energy release data')
+parser.add_argument('mcnpdata', help='Directory containing endf71x and ENDF71SaB')
+args = parser.parse_args()
+assert os.path.isdir(args.mcnpdata)
+
+# Get a list of all ACE files
+endf71x = glob.glob(os.path.join(args.mcnpdata, 'endf71x', '*', '*.71?nc'))
+endf71sab = glob.glob(os.path.join(args.mcnpdata, 'ENDF71SaB' , '*.2?t'))
+
+# There's a bug in H-Zr at 1200 K
+endf71sab.remove(os.path.join(args.mcnpdata, 'ENDF71SaB' , 'h-zr.27t'))
+
+# Group together tables for the same nuclide
+suffixes = defaultdict(list)
+for filename in sorted(endf71x + endf71sab):
+    dirname, basename = os.path.split(filename)
+    zaid, xs = basename.split('.')
+    suffixes[os.path.join(dirname, zaid)].append(xs)
+
+# Create output directory if it doesn't exist
+if not os.path.isdir(args.destination):
+    os.mkdir(args.destination)
+
+library = openmc.data.DataLibrary()
+
+for basename, xs_list in sorted(suffixes.items()):
+    # Convert first temperature for the table
+    filename = '.'.join((basename, xs_list[0]))
+    print('Converting: ' + filename)
+    if filename.endswith('t'):
+        data = openmc.data.ThermalScattering.from_ace(filename)
+    else:
+        data = openmc.data.IncidentNeutron.from_ace(filename, 'mcnp')
+
+        # Add fission energy release data, if available
+        if args.fission_energy_release is not None:
+            fer = openmc.data.FissionEnergyRelease.from_compact_hdf5(
+                args.fission_energy_release, data)
+            if fer is not None:
+                data.fission_energy = fer
+
+    # For each higher temperature, add cross sections to the existing table
+    for xs in xs_list[1:]:
+        filename = '.'.join((basename, xs))
+        print('Adding: ' + filename)
+        if filename.endswith('t'):
+            data.add_temperature_from_ace(filename)
+        else:
+            data.add_temperature_from_ace(filename, 'mcnp')
+
+    # Export HDF5 file
+    h5_file = os.path.join(args.destination, data.name + '.h5')
+    print('Writing {}...'.format(h5_file))
+    data.export_to_hdf5(h5_file, 'w')
+
+    # Register with library
+    library.register_file(h5_file)
+
+# Write cross_sections.xml
+libpath = os.path.join(args.destination, 'cross_sections.xml')
+library.export_to_xml(libpath)
diff --git a/data/get_jeff_data.py b/data/get_jeff_data.py
index fa9394850..d0e5839b9 100755
--- a/data/get_jeff_data.py
+++ b/data/get_jeff_data.py
@@ -2,14 +2,13 @@
 
 from __future__ import print_function
 import os
-import shutil
-import subprocess
+from collections import defaultdict
 import sys
 import tarfile
 import zipfile
 import glob
-import hashlib
 import argparse
+from string import digits
 
 import openmc.data
 
@@ -26,23 +25,18 @@ else:
 
 download_warning = """
 WARNING: This script will download approximately 9 GB of data. Extracting and
-processing the data may require as much as 30 GB of additional free disk
+processing the data may require as much as 40 GB of additional free disk
 space. Note that if you don't need all 11 temperatures, you can modify the
 'files' list in the script to download only the data you want.
 
 Are you sure you want to continue? ([y]/n)
 """
 
-thermal_suffix = {20: '01t', 100: '02t', 293: '03t', 296: '03t', 323: '04t',
-                  350: '05t', 373: '06t', 400: '07t', 423: '08t', 473: '09t',
-                  500: '10t', 523: '11t', 573: '12t', 600: '13t', 623: '14t',
-                  643: '15t', 647: '15t', 700: '16t', 773: '17t', 800: '18t',
-                  1000: '19t', 1200: '20t', 1600: '21t', 2000: '22t',
-                  3000: '23t'}
-
 parser = argparse.ArgumentParser()
 parser.add_argument('-b', '--batch', action='store_true',
                     help='supresses standard in')
+parser.add_argument('-d', '--destination', default='jeff-3.2-hdf5',
+                    help='Directory to create new library in')
 args = parser.parse_args()
 
 response = askuser(download_warning) if not args.batch else 'y'
@@ -130,21 +124,6 @@ for f in files:
             for path in glob.glob(os.path.join('jeff-3.2', 'ACEs_293K', '*-293.ACE')):
                 os.remove(path)
 
-# ==============================================================================
-# FIX ERRORS
-
-# A few nuclides at 400K has 03c instead of 04c
-print('Assigning new cross section identifiers...')
-wrong_nuclides = ['Mn55', 'Mo95', 'Nb93', 'Pd105', 'Pu239', 'Pu240', 'U235',
-                  'U238', 'Y89']
-for nuc in wrong_nuclides:
-    path = os.path.join('jeff-3.2', 'ACEs_400K', nuc + '.ACE')
-    print('    Fixing {} (03c --> 04c)...'.format(path))
-    if os.path.isfile(path):
-        text = open(path, 'r').read()
-        text = text[:7] + '04c' + text[10:]
-        open(path, 'w').write(text)
-
 # ==============================================================================
 # CHANGE ZAID FOR METASTABLES
 
@@ -158,50 +137,90 @@ for path in metastables:
         open(path, 'w').write(text)
 
 # ==============================================================================
-# CHANGE IDENTIFIER FOR S(A,B) TABLES
+# GENERATE HDF5 LIBRARY -- NEUTRON FILES
 
-thermals = glob.glob(os.path.join('jeff-3.2', 'ANNEX_6_3_STLs', '**', '*.ace'))
-for path in thermals:
-    print('    Fixing {} (unique suffix)...'.format(path))
-    basename = os.path.basename(path)
-    temperature = int(basename.split('-')[1][:-4])
-    text = open(path, 'r').read()
-    text = text[:7] + thermal_suffix[temperature] + text[10:]
-    open(path, 'w').write(text)
+# Get a list of all ACE files
+neutron_files = glob.glob(os.path.join('jeff-3.2', '*', '*.ACE'))
+
+# Group together tables for same nuclide
+tables = defaultdict(list)
+for filename in sorted(neutron_files):
+    dirname, basename = os.path.split(filename)
+    name = basename.split('.')[0]
+    tables[name].append(filename)
+
+# Sort temperatures from lowest to highest
+for name, filenames in sorted(tables.items()):
+    filenames.sort(key=lambda x: int(
+        x.split(os.path.sep)[1].split('_')[1][:-1]))
+
+# Create output directory if it doesn't exist
+if not os.path.isdir(args.destination):
+    os.mkdir(args.destination)
+
+library = openmc.data.DataLibrary()
+
+for name, filenames in sorted(tables.items()):
+    # Convert first temperature for the table
+    print('Converting: ' + filenames[0])
+    data = openmc.data.IncidentNeutron.from_ace(filenames[0])
+
+    # For each higher temperature, add cross sections to the existing table
+    for filename in filenames[1:]:
+        print('Adding: ' + filename)
+        data.add_temperature_from_ace(filename)
+
+    # Export HDF5 file
+    h5_file = os.path.join(args.destination, data.name + '.h5')
+    print('Writing {}...'.format(h5_file))
+    data.export_to_hdf5(h5_file, 'w')
+
+    # Register with library
+    library.register_file(h5_file)
 
 # ==============================================================================
-# CONVERT TO BINARY TO SAVE DISK SPACE
+# GENERATE HDF5 LIBRARY -- S(A,B) FILES
 
-# get a list of all ACE files
-ace_files = (glob.glob(os.path.join('jeff-3.2', '**', '*.ACE')) +
-             glob.glob(os.path.join('jeff-3.2', 'ANNEX_6_3_STLs', '**', '*.ace')))
+sab_files = glob.glob(os.path.join('jeff-3.2', 'ANNEX_6_3_STLs', '*', '*.ace'))
 
-# Ask user to convert
-if not args.batch:
-    response = askuser('Convert ACE files to binary? ([y]/n) ')
-else:
-    response = 'y'
+# Group together tables for same nuclide
+tables = defaultdict(list)
+for filename in sorted(sab_files):
+    dirname, basename = os.path.split(filename)
+    name = basename.split('-')[0]
+    tables[name].append(filename)
 
-# Convert files if requested
-if not response or response.lower().startswith('y'):
-    for f in ace_files:
-        print('    Converting {}...'.format(f))
-        openmc.data.ace.ascii_to_binary(f, f)
+# Sort temperatures from lowest to highest
+for name, filenames in sorted(tables.items()):
+    filenames.sort(key=lambda x: int(
+        os.path.split(x)[1].split('-')[1].split('.')[0]))
 
-# ==============================================================================
-# PROMPT USER TO GENERATE HDF5 LIBRARY
+for name, filenames in sorted(tables.items()):
+    # Convert first temperature for the table
+    print('Converting: ' + filenames[0])
 
-# Ask user to convert
-if not args.batch:
-    response = askuser('Generate HDF5 library? ([y]/n) ')
-else:
-    response = 'y'
+    # Take numbers out of table name, e.g. lw10.32t -> lw.32t
+    table = openmc.data.ace.get_table(filenames[0])
+    name, xs = table.name.split('.')
+    table.name = '.'.join((name.strip(digits), xs))
+    data = openmc.data.ThermalScattering.from_ace(table)
 
-# Convert files if requested
-if not response or response.lower().startswith('y'):
-    # Ensure 'import openmc.data' works in the openmc-ace-to-xml script
-    env = os.environ.copy()
-    env['PYTHONPATH'] = os.path.join(os.getcwd(), os.pardir)
+    # For each higher temperature, add cross sections to the existing table
+    for filename in filenames[1:]:
+        print('Adding: ' + filename)
+        table = openmc.data.ace.get_table(filename)
+        name, xs = table.name.split('.')
+        table.name = '.'.join((name.strip(digits), xs))
+        data.add_temperature_from_ace(table)
 
-    subprocess.call(['../scripts/openmc-ace-to-hdf5', '-d', 'jeff-3.2-hdf5']
-                    + sorted(ace_files), env=env)
+    # Export HDF5 file
+    h5_file = os.path.join(args.destination, data.name + '.h5')
+    print('Writing {}...'.format(h5_file))
+    data.export_to_hdf5(h5_file, 'w')
+
+    # Register with library
+    library.register_file(h5_file)
+
+# Write cross_sections.xml
+libpath = os.path.join(args.destination, 'cross_sections.xml')
+library.export_to_xml(libpath)
diff --git a/docs/source/io_formats/summary.rst b/docs/source/io_formats/summary.rst
index 8af4f2398..435cb45c8 100644
--- a/docs/source/io_formats/summary.rst
+++ b/docs/source/io_formats/summary.rst
@@ -4,7 +4,7 @@
 Summary File Format
 ===================
 
-The current revision of the summary file format is 1.
+The current revision of the summary file format is 4.
 
 **/filetype** (*char[]*)
 
@@ -129,7 +129,16 @@ The current revision of the summary file format is 1.
 
 **/geometry/cells/cell <uid>/distribcell_index** (*int*)
 
-    Index of this cell in distribcell filter arrays.
+    Index of this cell in distribcell arrays. Only present if this cell is
+    listed in a distribcell filter or if it uses distributed materials.
+
+**/geometry/cells/cell <uid>/paths** (*char[][]*)
+
+    The paths traversed through the CSG tree to reach each distribcell
+    instance. This consists of the integer IDs for each universe, cell and
+    lattice delimited by '->'. Each lattice cell is specified by its (x,y) or
+    (x,y,z) indices. Only present if this cell is listed in a distribcell filter
+    or if it uses distributed materials.
 
 **/geometry/surfaces/surface <uid>/index** (*int*)
 
@@ -244,90 +253,6 @@ The current revision of the summary file format is 1.
 
     Names of S(:math:`\alpha`,:math:`\beta`) tables assigned to the material.
 
-**/tallies/n_tallies** (*int*)
-
-    Number of tallies in the problem.
-
-**/tallies/n_meshes** (*int*)
-
-    Number of meshes in the problem.
-
-**/tallies/mesh <uid>/index** (*int*)
-
-    Index in the meshes array used internally in OpenMC.
-
-**/tallies/mesh <uid>/type** (*char[]*)
-
-    Type of the mesh. The only valid option is currently 'regular'.
-
-**/tallies/mesh <uid>/dimension** (*int[]*)
-
-    Number of mesh cells in each direction.
-
-**/tallies/mesh <uid>/lower_left** (*double[]*)
-
-    Coordinates of the lower-left corner of the mesh.
-
-**/tallies/mesh <uid>/upper_right** (*double[]*)
-
-    Coordinates of the upper-right corner of the mesh.
-
-**/tallies/mesh <uid>/width** (*double[]*)
-
-    Width of a single mesh cell in each direction.
-
-**/tallies/tally <uid>/index** (*int*)
-
-    Index in tallies array used internally in OpenMC.
-
 **/tallies/tally <uid>/name** (*char[]*)
 
     Name of the tally.
-
-**/tallies/tally <uid>/n_filters** (*int*)
-
-    Number of filters applied to the tally.
-
-**/tallies/tally <uid>/filter <j>/type** (*char[]*)
-
-    Type of the j-th filter. Can be 'universe', 'material', 'cell', 'cellborn',
-    'surface', 'mesh', 'energy', 'energyout', or 'distribcell'.
-
-**/tallies/tally <uid>/filter <j>/offset** (*int*)
-
-    Filter offset (used for distribcell filter).
-
-**/tallies/tally <uid>/filter <j>/paths** (*char[][]*)
-
-    The paths traversed through the CSG tree to reach each distribcell
-    instance (for 'distribcell' filters only). This consists of the integer
-    IDs for each universe, cell and lattice delimited by '->'. Each lattice
-    cell is specified by its (x,y) or (x,y,z) indices.
-
-**/tallies/tally <uid>/filter <j>/n_bins** (*int*)
-
-    Number of bins for the j-th filter.
-
-**/tallies/tally <uid>/filter <j>/bins** (*int[]* or *double[]*)
-
-    Value for each filter bin of this type.
-
-**/tallies/tally <uid>/nuclides** (*char[][]*)
-
-    Array of nuclides to tally. Note that if no nuclide is specified in the user
-    input, a single 'total' nuclide appears here.
-
-**/tallies/tally <uid>/n_score_bins** (*int*)
-
-    Number of scoring bins for a single nuclide. In general, this can be greater
-    than the number of user-specified scores since each score might have
-    multiple scoring bins, e.g., scatter-PN.
-
-**/tallies/tally <uid>/moment_orders** (*char[][]*)
-
-    Tallying moment orders for Legendre and spherical harmonic tally expansions
-    (*e.g.*, 'P2', 'Y1,2', etc.).
-
-**/tallies/tally <uid>/score_bins** (*char[][]*)
-
-    Scoring bins for the tally.
diff --git a/docs/source/pythonapi/examples/nuclear-data.ipynb b/docs/source/pythonapi/examples/nuclear-data.ipynb
index 82be4a113..e3691d11c 100644
--- a/docs/source/pythonapi/examples/nuclear-data.ipynb
+++ b/docs/source/pythonapi/examples/nuclear-data.ipynb
@@ -52,7 +52,7 @@
     {
      "data": {
       "text/plain": [
-       "<IncidentNeutron: Gd157.71c>"
+       "<IncidentNeutron: Gd157>"
       ]
      },
      "execution_count": 2,
@@ -62,7 +62,7 @@
    ],
    "source": [
     "# Get filename for Gd-157\n",
-    "filename ='/home/smharper/nuclear-data/nndc-hdf5/Gd157_71c.h5'\n",
+    "filename ='/home/romano/openmc/data/nndc_hdf5/Gd157.h5'\n",
     "\n",
     "# Load HDF5 data into object\n",
     "gd157 = openmc.data.IncidentNeutron.from_hdf5(filename)\n",
@@ -110,7 +110,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To find the cross section at a particular energy, 1 eV for example, simply call the reaction's `xs` attribute at that energy. Note that our nuclear data uses MeV as the unit of energy."
+    "Cross sections for each reaction can be stored at multiple temperatures. To see what temperatures are available, we can look at the reaction's `xs` attribute."
    ]
   },
   {
@@ -123,7 +123,7 @@
     {
      "data": {
       "text/plain": [
-       "142.6474702147809"
+       "{'294K': <openmc.data.function.Sum at 0x7f878c931860>}"
       ]
      },
      "execution_count": 4,
@@ -132,14 +132,14 @@
     }
    ],
    "source": [
-    "total.xs(1e-6)"
+    "total.xs"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The `xs` attribute can also be called on an array of energies."
+    "To find the cross section at a particular energy, 1 eV for example, simply get the cross section at the appropriate temperature and then call it as a function. Note that our nuclear data uses MeV as the unit of energy."
    ]
   },
   {
@@ -152,7 +152,7 @@
     {
      "data": {
       "text/plain": [
-       "array([ 142.64747021,   38.65417611,  175.40019668])"
+       "142.6474702147809"
       ]
      },
      "execution_count": 5,
@@ -161,14 +161,14 @@
     }
    ],
    "source": [
-    "total.xs([1e-6, 2e-6, 3e-6])"
+    "total.xs['294K'](1e-6)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A quick way to plot cross sections is to use the `energy` attribute of `IncidentNeutron`. This gives an array of all the energy values used in cross section interpolation."
+    "The `xs` attribute can also be called on an array of energies."
    ]
   },
   {
@@ -181,8 +181,7 @@
     {
      "data": {
       "text/plain": [
-       "array([  1.00000000e-11,   1.03250000e-11,   1.06500000e-11, ...,\n",
-       "         1.95000000e+01,   1.99000000e+01,   2.00000000e+01])"
+       "array([ 142.64747021,   38.65417611,  175.40019668])"
       ]
      },
      "execution_count": 6,
@@ -191,7 +190,14 @@
     }
    ],
    "source": [
-    "gd157.energy"
+    "total.xs['294K']([1e-6, 2e-6, 3e-6])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A quick way to plot cross sections is to use the `energy` attribute of `IncidentNeutron`. This gives an array of all the energy values used in cross section interpolation for each temperature present."
    ]
   },
   {
@@ -204,18 +210,41 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x7f237f0f0810>"
+       "{'294K': array([  1.00000000e-11,   1.03250000e-11,   1.06500000e-11, ...,\n",
+       "          1.95000000e+01,   1.99000000e+01,   2.00000000e+01])}"
       ]
      },
      "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gd157.energy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x7f878a1cff28>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
      "data": {
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PtXKtkYiUIwUMqXhnnAG33AIrV66enhkw2reHZcsKz+ubb0IH+1df5X5enObP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\nMzvBzK40s06p9xr7DxV1+G2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Q9u3b+x5jsZ+tlhbH9xiNGh3B+PFdOfbY5yv8QF67tohly9bw8ceN2bXrWw45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\nAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f239c356b90>"
+       "<matplotlib.figure.Figure at 0x7f878c9314e0>"
       ]
      },
      "metadata": {},
@@ -223,7 +252,9 @@
     }
    ],
    "source": [
-    "plt.loglog(gd157.energy, total.xs(gd157.energy))\n",
+    "energies = gd157.energy['294K']\n",
+    "total_xs = total.xs['294K'](energies)\n",
+    "plt.loglog(energies, total_xs)\n",
     "plt.xlabel('Energy (MeV)')\n",
     "plt.ylabel('Cross section (b)')"
    ]
@@ -239,7 +270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
@@ -274,7 +305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -289,7 +320,7 @@
    ],
    "source": [
     "n2n = gd157[16]\n",
-    "print('Threshold = {} MeV'.format(n2n.threshold))"
+    "print('Threshold = {} MeV'.format(n2n.xs['294K'].x[0]))"
    ]
   },
   {
@@ -299,28 +330,6 @@
     "The (n,2n) cross section, like all basic cross sections, is represented by the `Tabulated1D` class. The energy and cross section values in the table can be directly accessed with the `x` and `y` attributes. Using the `x` and `y` has the nice benefit of automatically acounting for reaction thresholds."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<openmc.data.function.Tabulated1D at 0x7f237f748650>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "n2n.xs"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 11,
@@ -331,36 +340,16 @@
     {
      "data": {
       "text/plain": [
-       "(6.400881, 20.0)"
+       "{'294K': <openmc.data.function.Tabulated1D at 0x7f878c931cc0>}"
       ]
      },
      "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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KdSXV09NPwymnKCmISHFTYqhA3UgiIkoM35k/P2zKc+SRcUciIhIvJYaEUaOg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\nICIilSgxiIhIJf8f4IdhH/peRHEAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f237ef64c10>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
-    "plt.plot(n2n.xs.x, n2n.xs.y)\n",
-    "plt.xlabel('Energy (MeV)')\n",
-    "plt.ylabel('Cross section (b)')\n",
-    "plt.xlim((n2n.xs.x[0], n2n.xs.x[-1]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To get information on the energy and angle distribution of the neutrons emitted in the reaction, we need to look at the `products` attribute."
+    "n2n.xs"
    ]
   },
   {
@@ -373,17 +362,37 @@
     {
      "data": {
       "text/plain": [
-       "[<Product: neutron, emission=prompt, yield=2.0>,\n",
-       " <Product: photon, emission=prompt, yield=tabulated>]"
+       "(6.400881, 20.0)"
       ]
      },
      "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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LgEYFHjdMHPudu68qcP95MxthZvXcfUXRFxs/fnxkgZbkuuvC2oV+/U4o89w+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\n9zC0aV7vrgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8789b33dd8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "n2n.products"
+    "xs = n2n.xs['294K']\n",
+    "plt.plot(xs.x, xs.y)\n",
+    "plt.xlabel('Energy (MeV)')\n",
+    "plt.ylabel('Cross section (b)')\n",
+    "plt.xlim((xs.x[0], xs.x[-1]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To get information on the energy and angle distribution of the neutrons emitted in the reaction, we need to look at the `products` attribute."
    ]
   },
   {
@@ -396,7 +405,8 @@
     {
      "data": {
       "text/plain": [
-       "[<openmc.data.correlated.CorrelatedAngleEnergy at 0x7f237f766710>]"
+       "[<Product: neutron, emission=prompt, yield=polynomial>,\n",
+       " <Product: photon, emission=prompt, yield=tabulated>]"
       ]
      },
      "execution_count": 13,
@@ -404,6 +414,28 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "n2n.products"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<openmc.data.correlated.CorrelatedAngleEnergy at 0x7f878c8916a0>]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "neutron = n2n.products[0]\n",
     "neutron.distribution"
@@ -418,7 +450,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false
    },
@@ -426,39 +458,39 @@
     {
      "data": {
       "text/plain": [
-       "[<openmc.stats.univariate.Tabular at 0x7f237f766790>,\n",
-       " <openmc.stats.univariate.Tabular at 0x7f237f766810>,\n",
-       " <openmc.stats.univariate.Tabular at 0x7f237f7668d0>,\n",
-       " <openmc.stats.univariate.Tabular at 0x7f237f766990>,\n",
-       " <openmc.stats.univariate.Tabular at 0x7f237f766b10>,\n",
-       " <openmc.stats.univariate.Tabular at 0x7f237f766d50>,\n",
-       " <openmc.stats.univariate.Tabular at 0x7f237f6ed050>,\n",
-       " <openmc.stats.univariate.Tabular at 0x7f237f6ed3d0>,\n",
-       " <openmc.stats.univariate.Tabular at 0x7f237f6ed7d0>,\n",
-       " <openmc.stats.univariate.Tabular at 0x7f237f6edc50>,\n",
-       " <openmc.stats.univariate.Tabular at 0x7f237f6f6190>,\n",
-       " <openmc.stats.univariate.Tabular at 0x7f237f6f6710>,\n",
-       " <openmc.stats.univariate.Tabular at 0x7f237f6f6c90>,\n",
-       " <openmc.stats.univariate.Tabular at 0x7f237f700310>,\n",
-       " <openmc.stats.univariate.Tabular at 0x7f237f700990>,\n",
-       " <openmc.stats.univariate.Tabular at 0x7f237f70a0d0>,\n",
-       " <openmc.stats.univariate.Tabular at 0x7f237f70a810>,\n",
-       " <openmc.stats.univariate.Tabular at 0x7f237f70afd0>,\n",
-       " <openmc.stats.univariate.Tabular at 0x7f237f713810>,\n",
-       " <openmc.stats.univariate.Tabular at 0x7f237f71d050>,\n",
-       " <openmc.stats.univariate.Tabular at 0x7f237f71d8d0>,\n",
-       " <openmc.stats.univariate.Tabular at 0x7f237f7251d0>,\n",
-       " <openmc.stats.univariate.Tabular at 0x7f237f725ad0>,\n",
-       " <openmc.stats.univariate.Tabular at 0x7f237f6af450>,\n",
-       " <openmc.stats.univariate.Tabular at 0x7f237f6afdd0>,\n",
-       " <openmc.stats.univariate.Tabular at 0x7f237f6b87d0>,\n",
-       " <openmc.stats.univariate.Tabular at 0x7f237f6c2210>,\n",
-       " <openmc.stats.univariate.Tabular at 0x7f237f6c2b50>,\n",
-       " <openmc.stats.univariate.Tabular at 0x7f237f6ca590>,\n",
-       " <openmc.stats.univariate.Tabular at 0x7f237f6cafd0>]"
+       "[<openmc.stats.univariate.Tabular at 0x7f878c8c92b0>,\n",
+       " <openmc.stats.univariate.Tabular at 0x7f878c8c93c8>,\n",
+       " <openmc.stats.univariate.Tabular at 0x7f878c8c9470>,\n",
+       " <openmc.stats.univariate.Tabular at 0x7f878c8c95c0>,\n",
+       " <openmc.stats.univariate.Tabular at 0x7f878c8c97b8>,\n",
+       " <openmc.stats.univariate.Tabular at 0x7f878c8c9a20>,\n",
+       " <openmc.stats.univariate.Tabular at 0x7f878c8c9d30>,\n",
+       " <openmc.stats.univariate.Tabular at 0x7f878c8d00f0>,\n",
+       " <openmc.stats.univariate.Tabular at 0x7f878c8d04e0>,\n",
+       " <openmc.stats.univariate.Tabular at 0x7f878c8d0940>,\n",
+       " <openmc.stats.univariate.Tabular at 0x7f878c8d0e10>,\n",
+       " <openmc.stats.univariate.Tabular at 0x7f878c8d6320>,\n",
+       " <openmc.stats.univariate.Tabular at 0x7f878c8d6898>,\n",
+       " <openmc.stats.univariate.Tabular at 0x7f878c8d6e48>,\n",
+       " <openmc.stats.univariate.Tabular at 0x7f878c8dd4a8>,\n",
+       " <openmc.stats.univariate.Tabular at 0x7f878c8ddb00>,\n",
+       " <openmc.stats.univariate.Tabular at 0x7f878c8e3208>,\n",
+       " <openmc.stats.univariate.Tabular at 0x7f878c8e3908>,\n",
+       " <openmc.stats.univariate.Tabular at 0x7f878c8ea048>,\n",
+       " <openmc.stats.univariate.Tabular at 0x7f878c8ea7b8>,\n",
+       " <openmc.stats.univariate.Tabular at 0x7f878c8eaf60>,\n",
+       " <openmc.stats.univariate.Tabular at 0x7f878c8f1780>,\n",
+       " <openmc.stats.univariate.Tabular at 0x7f878c8f1f98>,\n",
+       " <openmc.stats.univariate.Tabular at 0x7f878c8f7828>,\n",
+       " <openmc.stats.univariate.Tabular at 0x7f878c8fd0f0>,\n",
+       " <openmc.stats.univariate.Tabular at 0x7f878c8fd9b0>,\n",
+       " <openmc.stats.univariate.Tabular at 0x7f878c884208>,\n",
+       " <openmc.stats.univariate.Tabular at 0x7f878c884ac8>,\n",
+       " <openmc.stats.univariate.Tabular at 0x7f878c88a400>,\n",
+       " <openmc.stats.univariate.Tabular at 0x7f878c88ad30>]"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -477,16 +509,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
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Rm8NpInzR+/mjLRe9n8UIg0FxeikIQM8qR5IKYDaAN5rToK+vb4tGFqmFqUgvTkdOaY78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\nZNvP5+FeXaSXaVO63F7ZfRHwsqpuALLtqsNGiDAJw/A5+xN+nao+B6D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SIwT6Eq2AnkTrK6+8wvjx43nyyScJDQ0lMDCw1hKt9aFVGAyNKDdRSkwk2dER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\nMAzDMAyv/B9ZBCMoWOfF0wAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f237ef48610>"
+       "<matplotlib.figure.Figure at 0x7f8789ab78d0>"
       ]
      },
      "metadata": {},
@@ -512,7 +544,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false
    },
@@ -542,7 +574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
@@ -550,10 +582,10 @@
     {
      "data": {
       "text/plain": [
-       "<openmc.data.function.Sum at 0x7f237f7a9f90>"
+       "{'294K': <openmc.data.function.Sum at 0x7f878c931828>}"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -573,7 +605,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {
     "collapsed": false
    },
@@ -581,18 +613,18 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x7f237eccbf10>"
+       "<matplotlib.text.Text at 0x7f8789a93d68>"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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i448/jjx1q+Wbb77BffexS0AMHDgQhw8fjnfKWUEsH4jRaMRFF12ERx99VHMT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\nxAnEzMwKcQIxM7NC/g/XBW/wr41qsAAAAABJRU5ErkJggg==\n",
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mzy7/8+bEI79MBh9cCYQCs+prhZu0AG7W4sjhAiRGBAHi8/l6hABJRxhvPKRaAxk9enRc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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f237ecb7a10>"
+       "<matplotlib.figure.Figure at 0x7f878a19d710>"
       ]
      },
      "metadata": {},
@@ -605,7 +637,7 @@
     "cm = matplotlib.cm.Spectral_r\n",
     "\n",
     "# Determine size of probability tables\n",
-    "urr = gd157.urr\n",
+    "urr = gd157.urr['294K']\n",
     "n_energy = urr.table.shape[0]\n",
     "n_band = urr.table.shape[2]\n",
     "\n",
@@ -644,7 +676,7 @@
     "                     color=cm(value)))\n",
     "\n",
     "# Overlay total cross section\n",
-    "ax.plot(gd157.energy, total.xs(gd157.energy), 'k')\n",
+    "ax.plot(gd157.energy['294K'], total.xs['294K'](gd157.energy['294K']), 'k')\n",
     "\n",
     "# Make plot pretty and labeled\n",
     "ax.set_xlim(1e-6, 1e-1)\n",
@@ -666,7 +698,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {
     "collapsed": false
    },
@@ -674,16 +706,16 @@
     {
      "data": {
       "text/plain": [
-       "<IncidentNeutron: Gd157.71c>"
+       "<IncidentNeutron: Gd157>"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "filename = '/home/smharper/nuclear-data/nndc/293.6K/Gd_157_293.6K.ace'\n",
+    "filename = '/opt/data/ace/nndc/293.6K/Gd_157_293.6K.ace'\n",
     "gd157_ace = openmc.data.IncidentNeutron.from_ace(filename)\n",
     "gd157_ace"
    ]
@@ -697,9 +729,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
    "outputs": [],
    "source": [
@@ -715,7 +747,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {
     "collapsed": false
    },
@@ -728,14 +760,14 @@
        "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.])"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "gd157_reconstructed = openmc.data.IncidentNeutron.from_hdf5('gd157.h5')\n",
-    "gd157_ace[16].xs.y - gd157_reconstructed[16].xs.y"
+    "gd157_ace[16].xs['294K'].y - gd157_reconstructed[16].xs['294K'].y"
    ]
   },
   {
@@ -747,7 +779,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {
     "collapsed": false
    },
@@ -771,7 +803,7 @@
    ],
    "source": [
     "h5file = h5py.File('gd157.h5', 'r')\n",
-    "main_group = h5file['Gd157.71c/reactions']\n",
+    "main_group = h5file['Gd157/reactions']\n",
     "for name, obj in sorted(list(main_group.items()))[:10]:\n",
     "    if 'reaction_' in name:\n",
     "        print('{}, {}'.format(name, obj.attrs['label'].decode()))"
@@ -779,7 +811,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false
    },
@@ -788,9 +820,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[<HDF5 dataset \"xs\": shape (37,), type \"<f8\">,\n",
-      " <HDF5 group \"/Gd157.71c/reactions/reaction_016/product_0\" (2 members)>,\n",
-      " <HDF5 group \"/Gd157.71c/reactions/reaction_016/product_1\" (2 members)>]\n"
+      "[<HDF5 group \"/Gd157/reactions/reaction_016/product_0\" (2 members)>,\n",
+      " <HDF5 group \"/Gd157/reactions/reaction_016/294K\" (1 members)>,\n",
+      " <HDF5 group \"/Gd157/reactions/reaction_016/product_1\" (2 members)>]\n"
      ]
     }
    ],
@@ -808,7 +840,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -832,33 +864,33 @@
        "         7.77740000e-01])"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "n2n_group['xs'].value"
+    "n2n_group['294K/xs'].value"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.12"
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/pythonapi/index.rst b/docs/source/pythonapi/index.rst
index d8eb5200a..16c90bcbc 100644
--- a/docs/source/pythonapi/index.rst
+++ b/docs/source/pythonapi/index.rst
@@ -366,6 +366,7 @@ Core Classes
     openmc.data.ThermalScattering
     openmc.data.CoherentElastic
     openmc.data.FissionEnergyRelease
+    openmc.data.DataLibrary
 
 Angle-Energy Distributions
 --------------------------
diff --git a/docs/source/usersguide/install.rst b/docs/source/usersguide/install.rst
index 457c5efd8..3dab08e9d 100644
--- a/docs/source/usersguide/install.rst
+++ b/docs/source/usersguide/install.rst
@@ -383,14 +383,16 @@ Cross Section Configuration
 ---------------------------
 
 In order to run a simulation with OpenMC, you will need cross section data for
-each nuclide or material in your problem. OpenMC can be run in
-continuous-energy or multi-group mode.
+each nuclide or material in your problem. OpenMC can be run in continuous-energy
+or multi-group mode.
 
-In continuous-energy mode OpenMC uses ACE format cross sections; in this case
-you can use nuclear data that was processed with NJOY_, such as that
-distributed with MCNP_ or Serpent_.  Several sources provide free processed
-ACE data as described below. The TALYS-based evaluated nuclear data library,
-TENDL_, is also openly available in ACE format.
+In continuous-energy mode, OpenMC uses a native HDF5 format to store all nuclear
+data. If you have ACE format data that was produced with NJOY_, such as that
+distributed with MCNP_ or Serpent_, it can be converted to the HDF5 format using
+the :ref:`openmc-ace-to-hdf5 <other_cross_sections>` script distributed with
+OpenMC.  Several sources provide openly available ACE data as described
+below. The TALYS-based evaluated nuclear data library, TENDL_, is also available
+in ACE format.
 
 In multi-group mode, OpenMC utilizes an XML-based library format which can be
 used to describe nuclide- or material-specific quantities.
@@ -400,8 +402,8 @@ Using ENDF/B-VII.1 Cross Sections from NNDC
 
 The NNDC_ provides ACE data from the ENDF/B-VII.1 neutron and thermal scattering
 sublibraries at four temperatures processed using NJOY_. To use this data with
-OpenMC, a script is provided with OpenMC that will automatically download,
-extract, and set up a confiuration file:
+OpenMC, a script is provided with OpenMC that will automatically download and
+extract the ACE data, fix any deficiencies, and create an HDF5 library:
 
 .. code-block:: sh
 
@@ -410,56 +412,99 @@ extract, and set up a confiuration file:
 
 At this point, you should set the :envvar:`OPENMC_CROSS_SECTIONS` environment
 variable to the absolute path of the file
-``openmc/data/nndc/cross_sections.xml``. This cross section set is used by the
-test suite.
+``openmc/data/nndc_hdf5/cross_sections.xml``. This cross section set is used by
+the test suite.
 
 Using JEFF Cross Sections from OECD/NEA
 ---------------------------------------
 
-The NEA_ provides processed ACE data from the JEFF_ nuclear library upon
-request. A DVD of the data can be requested here_. To use this data with OpenMC,
-the following steps must be taken:
+The NEA_ provides processed ACE data from the JEFF_ library. To use this data
+with OpenMC, a script is provided with OpenMC that will automatically download
+and extract the ACE data, fix any deficiencies, and create an HDF5 library.
 
-1. Copy and unzip the data on the DVD to a directory on your computer.
-2. In the root directory, a file named ``xsdir``, or some variant thereof,
-   should be present. This file contains a listing of all the cross sections and
-   is used by MCNP. This file should be converted to a ``cross_sections.xml``
-   file for use with OpenMC. A utility is provided in the OpenMC distribution
-   for this purpose:
+.. code-block:: sh
 
-   .. code-block:: sh
+    cd openmc/data
+    python get_jeff_data.py
 
-       openmc/scripts/openmc-xsdir-to-xml xsdir31 cross_sections.xml
-
-3. In the converted ``cross_sections.xml`` file, change the contents of the
-   <directory> element to the absolute path of the directory containing the
-   actual ACE files.
-4. Additionally, you may need to change any occurrences of upper-case "ACE"
-   within the ``cross_sections.xml`` file to lower-case.
-5. Either set the :ref:`cross_sections` in a settings.xml file or the
-   :envvar:`OPENMC_CROSS_SECTIONS` environment variable to the absolute path of
-   the ``cross_sections.xml`` file.
+At this point, you should set the :envvar:`OPENMC_CROSS_SECTIONS` environment
+variable to the absolute path of the file
+``openmc/data/jeff-3.2-hdf5/cross_sections.xml``.
 
 Using Cross Sections from MCNP
 ------------------------------
 
-To use cross sections distributed with MCNP, change the <directory> element in
-the ``cross_sections.xml`` file in the root directory of the OpenMC distribution
-to the location of the MCNP cross sections. Then, either set the
-:ref:`cross_sections` in a settings.xml file or the
-:envvar:`OPENMC_CROSS_SECTIONS` environment variable to the absolute path of
-the ``cross_sections.xml`` file.
+OpenMC is provided with a script that will automatically convert ENDF/B-VII.0
+and ENDF/B-VII.1 ACE data that is provided with MCNP5 or MCNP6. To convert the
+ENDF/B-VII.0 ACE files (``endf70[a-k]`` and ``endf70sab``) into the native HDF5
+format, run the following:
 
-Using Cross Sections from Serpent
----------------------------------
+.. code-block:: sh
+
+    cd openmc/data
+    python convert_mcnp_endf70.py /path/to/mcnpdata/
+
+where ``/path/to/mcnpdata`` is the directory containing the ``endf70[a-k]``
+files.
+
+To convert the ENDF/B-VII.1 ACE files (the endf71x and ENDF71SaB libraries), use
+the following script:
+
+.. code-block:: sh
+
+    cd openmc/data
+    python convert_mcnp_endf71.py /path/to/mcnpdata
+
+where ``/path/to/mcnpdata`` is the directory containing the ``endf71x`` and
+``ENDF71SaB`` directories.
+
+.. _other_cross_sections:
+
+Using Other Cross Sections
+--------------------------
+
+If you have a library of ACE format cross sections other than those listed above
+that you need to convert to OpenMC's HDF5 format, the ``openmc-ace-to-hdf5``
+script can be used. There are four different ways you can specify ACE libraries
+that are to be converted:
+
+1. List each ACE library as a positional argument. This is very useful in
+   conjunction with the usual shell utilities (ls, find, etc.).
+2. Use the --xml option to specify a pre-v0.9 cross_sections.xml file.
+3. Use the --xsdir option to specify a MCNP xsdir file.
+4. Use the --xsdata option to specify a Serpent xsdata file.
+
+The script does not use any extra information from cross_sections.xml/ xsdir/
+xsdata files to determine whether the nuclide is metastable. Instead, the
+--metastable argument can be used to specify whether the ZAID naming convention
+follows the NNDC data convention (1000*Z + A + 300 + 100*m), or the MCNP data
+convention (essentially the same as NNDC, except that the first metastable state
+of Am242 is 95242 and the ground state is 95642).
+
+The ``openmc-ace-to-hdf5`` script has the following command-line flags:
+
+-h, --help            show this help message and exit
+
+-d DESTINATION, --destination DESTINATION
+                      Directory to create new library in (default: .)
+
+-m META, --metastable META
+                      How to interpret ZAIDs for metastable nuclides. META
+                      can be either 'nndc' or 'mcnp'. (default: nndc)
+
+--xml XML             Old-style cross_sections.xml that lists ACE libraries
+                      (default: None)
+
+--xsdir XSDIR         MCNP xsdir file that lists ACE libraries (default:
+                      None)
+
+--xsdata XSDATA       Serpent xsdata file that lists ACE libraries (default:
+                      None)
+
+--fission_energy_release FISSION_ENERGY_RELEASE
+                      HDF5 file containing fission energy release data
+                      (default: None)
 
-To use cross sections distributed with Serpent, change the <directory> element
-in the ``cross_sections_serpent.xml`` file in the root directory of the OpenMC
-distribution to the location of the Serpent cross sections. Then, either set the
-:ref:`cross_sections` in a settings.xml file or the
-:envvar:`OPENMC_CROSS_SECTIONS` environment variable to the absolute path of
-the ``cross_sections_serpent.xml``
-file.
 
 Using Multi-Group Cross Sections
 --------------------------------
@@ -471,14 +516,13 @@ However, if the user has obtained or generated their own library, the user
 should set the :envvar:`OPENMC_MG_CROSS_SECTIONS` environment variable
 to the absolute path of the file library expected to used most frequently.
 
-.. _NJOY: http://t2.lanl.gov/nis/codes.shtml
+.. _NJOY: http://t2.lanl.gov/nis/codes/NJOY12/
 .. _NNDC: http://www.nndc.bnl.gov/endf/b7.1/acefiles.html
 .. _NEA: http://www.oecd-nea.org
-.. _JEFF: http://www.oecd-nea.org/dbdata/jeff/
-.. _here: http://www.oecd-nea.org/dbdata/pubs/jeff312-cd.html
+.. _JEFF: https://www.oecd-nea.org/dbforms/data/eva/evatapes/jeff_32/
 .. _MCNP: http://mcnp.lanl.gov
 .. _Serpent: http://montecarlo.vtt.fi
-.. _TENDL: ftp://ftp.nrg.eu/pub/www/talys/tendl2012/tendl2012.html
+.. _TENDL: https://tendl.web.psi.ch/tendl_2015/tendl2015.html
 
 --------------
 Running OpenMC
diff --git a/openmc/cell.py b/openmc/cell.py
index fe8b4a52c..1cfc09f37 100644
--- a/openmc/cell.py
+++ b/openmc/cell.py
@@ -85,6 +85,9 @@ class Cell(object):
         Array of offsets used for distributed cell searches
     distribcell_index : int
         Index of this cell in distribcell arrays
+    distribcell_paths : list of str
+        The paths traversed through the CSG tree to reach each distribcell
+        instance
     volume_information : dict
         Estimate of the volume and total number of atoms of each nuclide from a
         stochastic volume calculation. This information is set with the
@@ -104,6 +107,7 @@ class Cell(object):
         self._translation = None
         self._offsets = None
         self._distribcell_index = None
+        self._distribcell_paths = None
         self._volume_information = None
 
     def __contains__(self, point):
@@ -217,6 +221,10 @@ class Cell(object):
     def distribcell_index(self):
         return self._distribcell_index
 
+    @property
+    def distribcell_paths(self):
+        return self._distribcell_paths
+
     @property
     def volume_information(self):
         return self._volume_information
@@ -291,6 +299,7 @@ class Cell(object):
 
     @temperature.setter
     def temperature(self, temperature):
+        # Make sure temperatures are positive
         cv.check_type('cell temperature', temperature, (Iterable, Real))
         if isinstance(temperature, Iterable):
             cv.check_type('cell temperature', temperature, Iterable, Real)
@@ -298,7 +307,15 @@ class Cell(object):
                 cv.check_greater_than('cell temperature', T, 0.0, True)
         else:
             cv.check_greater_than('cell temperature', temperature, 0.0, True)
-        self._temperature = temperature
+
+        # If this cell is filled with a universe or lattice, propagate
+        # temperatures to all cells contained. Otherwise, simply assign it.
+        if self.fill_type in ('universe', 'lattice'):
+            for c in self.get_all_cells().values():
+                if c.fill_type == 'material':
+                    c._temperature = temperature
+        else:
+            self._temperature = temperature
 
     @offsets.setter
     def offsets(self, offsets):
@@ -316,6 +333,12 @@ class Cell(object):
         cv.check_type('distribcell index', ind, Integral)
         self._distribcell_index = ind
 
+    @distribcell_paths.setter
+    def distribcell_paths(self, distribcell_paths):
+        cv.check_iterable_type('distribcell_paths', distribcell_paths,
+                               basestring)
+        self._distribcell_paths = distribcell_paths
+
     def add_surface(self, surface, halfspace):
         """Add a half-space to the list of half-spaces whose intersection defines the
         cell.
@@ -427,9 +450,8 @@ class Cell(object):
         else:
             if self.volume_information is not None:
                 volume = self.volume_information['volume'][0]
-                for full_name, atoms in self.volume_information['atoms']:
-                    name, xs = full_name.split('.')
-                    nuclide = openmc.Nuclide(name, xs)
+                for name, atoms in self.volume_information['atoms']:
+                    nuclide = openmc.Nuclide(name)
                     density = 1.0e-24 * atoms[0]/volume  # density in atoms/b-cm
                     nuclides[name] = (nuclide, density)
             else:
diff --git a/openmc/data/library.py b/openmc/data/library.py
index 49d1c78f6..0485af064 100644
--- a/openmc/data/library.py
+++ b/openmc/data/library.py
@@ -8,20 +8,50 @@ from openmc.clean_xml import clean_xml_indentation
 
 
 class DataLibrary(EqualityMixin):
+    """Collection of cross section data libraries.
+
+    Attributes
+    ----------
+    libraries : list of dict
+        List in which each item is a dictionary summarizing cross section data
+        from a single file. The dictionary has keys 'path', 'type', and
+        'materials'.
+
+    """
+
     def __init__(self):
         self.libraries = []
 
-    def register_file(self, filename, filetype='neutron'):
+    def register_file(self, filename):
+        """Register a file with the data library.
+
+        Parameters
+        ----------
+        filename : str
+            Path to the file to be registered.
+
+        """
         h5file = h5py.File(filename, 'r')
 
         materials = []
+        filetype = 'neutron'
         for name in h5file:
+            if name.startswith('c_'):
+                filetype = 'thermal'
             materials.append(name)
 
         library = {'path': filename, 'type': filetype, 'materials': materials}
         self.libraries.append(library)
 
     def export_to_xml(self, path='cross_sections.xml'):
+        """Export cross section data library to an XML file.
+
+        Parameters
+        ----------
+        path : str
+            Path to file to write. Defaults to 'cross_sections.xml'.
+
+        """
         root = ET.Element('cross_sections')
 
         # Determine common directory for library paths
diff --git a/openmc/data/neutron.py b/openmc/data/neutron.py
index 2431ba74a..c638dfd9b 100644
--- a/openmc/data/neutron.py
+++ b/openmc/data/neutron.py
@@ -320,11 +320,11 @@ class IncidentNeutron(EqualityMixin):
 
         # Add normal and summed reactions
         for mt in chain(data.reactions, data.summed_reactions):
-            if mt not in self:
-                raise ValueError("Tried to add cross sections for MT={} at T={}"
-                                 " but this reaction doesn't exist.".format(
-                                     mt, strT))
-            self[mt].xs[strT] = data[mt].xs[strT]
+            if mt in self:
+                self[mt].xs[strT] = data[mt].xs[strT]
+            else:
+                warn("Tried to add cross sections for MT={} at T={} but this "
+                     "reaction doesn't exist.".format(mt, strT))
 
         # Add probability tables
         if strT in data.urr:
diff --git a/openmc/data/thermal.py b/openmc/data/thermal.py
index bbdb12dad..90aaa1e6e 100644
--- a/openmc/data/thermal.py
+++ b/openmc/data/thermal.py
@@ -22,13 +22,13 @@ _THERMAL_NAMES = {'al': 'c_Al27', 'al27': 'c_Al27',
                   'bebeo': 'c_Be_in_BeO', 'be-o': 'c_Be_in_BeO', 'be/o': 'c_Be_in_BeO',
                   'benz': 'c_Benzine',
                   'cah': 'c_Ca_in_CaH2',
-                  'dd2o': 'c_D_in_D2O', 'hwtr': 'c_D_in_D2O',
+                  'dd2o': 'c_D_in_D2O', 'hwtr': 'c_D_in_D2O', 'hw': 'c_D_in_D2O',
                   'fe': 'c_Fe56', 'fe56': 'c_Fe56',
-                  'graph': 'c_Graphite', 'grph': 'c_Graphite',
+                  'graph': 'c_Graphite', 'grph': 'c_Graphite', 'gr': 'c_Graphite',
                   'hca': 'c_H_in_CaH2',
-                  'hch2': 'c_H_in_CH2', 'poly': 'c_H_in_CH2',
-                  'hh2o': 'c_H_in_H2O', 'lwtr': 'c_H_in_H2O',
-                  'hzrh': 'c_H_in_ZrH', 'h-zr': 'c_H_in_ZrH', 'h/zr': 'c_H_in_ZrH',
+                  'hch2': 'c_H_in_CH2', 'poly': 'c_H_in_CH2', 'pol': 'c_H_in_CH2',
+                  'hh2o': 'c_H_in_H2O', 'lwtr': 'c_H_in_H2O', 'lw': 'c_H_in_H2O',
+                  'hzrh': 'c_H_in_ZrH', 'h-zr': 'c_H_in_ZrH', 'h/zr': 'c_H_in_ZrH', 'hzr': 'c_H_in_ZrH',
                   'lch4': 'c_liquid_CH4', 'lmeth': 'c_liquid_CH4',
                   'mg': 'c_Mg24',
                   'obeo': 'c_O_in_BeO', 'o-be': 'c_O_in_BeO', 'o/be': 'c_O_in_BeO',
@@ -191,8 +191,6 @@ class ThermalScattering(EqualityMixin):
         self.name = name
         self.atomic_weight_ratio = atomic_weight_ratio
         self.kTs = kTs
-        self.temperatures = [str(int(round(kT / K_BOLTZMANN))) + "K"
-                             for kT in kTs]
         self.elastic_xs = {}
         self.elastic_mu_out = {}
         self.inelastic_xs = {}
@@ -208,6 +206,10 @@ class ThermalScattering(EqualityMixin):
         else:
             return "<Thermal Scattering Data>"
 
+    @property
+    def temperatures(self):
+        return ["{}K".format(int(round(kT / K_BOLTZMANN))) for kT in self.kTs]
+
     def export_to_hdf5(self, path, mode='a'):
         """Export table to an HDF5 file.
 
@@ -277,142 +279,36 @@ class ThermalScattering(EqualityMixin):
             Thermal scattering data
 
         """
-        if isinstance(ace_or_filename, Table):
-            ace = ace_or_filename
-        else:
-            ace = get_table(ace_or_filename)
+        data = ThermalScattering.from_ace(ace_or_filename, name)
 
-        # Get new name that is GND-consistent
-        ace_name, xs = ace.name.split('.')
-        if name is None:
-            if ace_name.lower() in _THERMAL_NAMES:
-                name = _THERMAL_NAMES[ace_name.lower()]
-            else:
-                # Make an educated guess? This actually works well for JEFF-3.2
-                # which stupidly uses names like lw00.32t, lw01.32t, etc. for
-                # different temperatures
-                matches = get_close_matches(
-                    ace_name.lower(), _THERMAL_NAMES.keys(), cutoff=0.5)
-                if len(matches) > 0:
-                    name = _THERMAL_NAMES[matches[0]]
-                else:
-                    # OK, we give up. Just use the ACE name.
-                    name = 'c_' + ace.name
-                warn('Thermal scattering material "{}" is not recognized. '
-                     'Assigning a name of {}.'.format(ace.name, name))
+        # Check if temprature already exists
+        strT = data.temperatures[0]
+        if strT in self.temperatures:
+            warn('S(a,b) data at T={} already exists.'.format(strT))
+            return
 
-        # If this ACE data matches the data within self then get the data
-        if ace.temperature not in self.kTs:
-            if name == self.name:
-                # Add temperature and kTs
-                strT = str(int(round(ace.temperature / K_BOLTZMANN))) + "K"
-                self.temperatures.append(strT)
-                self.kTs.append(ace.temperature)
+        # Check that name matches
+        if data.name != self.name:
+            raise ValueError('Data provided for an incorrect material.')
 
-                # Incoherent inelastic scattering cross section
-                idx = ace.jxs[1]
-                n_energy = int(ace.xss[idx])
-                energy = ace.xss[idx + 1: idx + 1 + n_energy]
-                xs = ace.xss[idx + 1 + n_energy: idx + 1 + 2 * n_energy]
-                self.inelastic_xs[strT] = Tabulated1D(energy, xs)
+        # Add temperature
+        self.kTs += data.kTs
 
-                # Make sure secondary_mode is always equal. This should always
-                # be the case, but to reduce future debugging should something
-                # change, this will alert the developers to the issue.
-                if ace.nxs[7] == 0:
-                    secondary_mode = 'equal'
-                elif ace.nxs[7] == 1:
-                    secondary_mode = 'skewed'
-                elif ace.nxs[7] == 2:
-                    secondary_mode = 'continuous'
+        # Add inelastic cross section and distributions
+        if strT in data.inelastic_xs:
+            self.inelastic_xs[strT] = data.inelastic_xs[strT]
+        if strT in data.inelastic_e_out:
+            self.inelastic_e_out[strT] = data.inelastic_e_out[strT]
+        if strT in data.inelastic_mu_out:
+            self.inelastic_mu_out[strT] = data.inelastic_mu_out[strT]
+        if strT in data.inelastic_dist:
+            self.inelastic_dist[strT] = data.inelastic_dist[strT]
 
-                if secondary_mode != self.secondary_mode:
-                    raise ValueError('Secondary Modes are inconsistent.')
-
-                n_energy_out = ace.nxs[4]
-                if self.secondary_mode in ('equal', 'skewed'):
-                    n_mu = ace.nxs[3]
-                    idx = ace.jxs[3]
-                    self.inelastic_e_out[strT] = \
-                        ace.xss[idx:idx + n_energy * n_energy_out * (n_mu + 2):
-                                n_mu + 2]
-                    self.inelastic_e_out[strT].shape = \
-                        (n_energy, n_energy_out)
-
-                    self.inelastic_mu_out[strT] = \
-                        ace.xss[idx:idx + n_energy * n_energy_out * (n_mu + 2)]
-                    self.inelastic_mu_out[strT].shape = \
-                        (n_energy, n_energy_out, n_mu + 2)
-                    self.inelastic_mu_out[strT] = \
-                        self.inelastic_mu_out[strT][:, :, 1:]
-                else:
-                    n_mu = ace.nxs[3] - 1
-                    idx = ace.jxs[3]
-                    locc = ace.xss[idx:idx + n_energy].astype(int)
-                    n_energy_out = \
-                        ace.xss[idx + n_energy:idx + 2 * n_energy].astype(int)
-                    energy_out = []
-                    mu_out = []
-                    for i in range(n_energy):
-                        idx = locc[i]
-
-                        # Outgoing energy distribution for incoming energy i
-                        e = ace.xss[idx + 1:idx + 1 + n_energy_out[i]*(n_mu + 3):
-                                    n_mu + 3]
-                        p = ace.xss[idx + 2:idx + 2 + n_energy_out[i]*(n_mu + 3):
-                                    n_mu + 3]
-                        c = ace.xss[idx + 3:idx + 3 + n_energy_out[i]*(n_mu + 3):
-                                    n_mu + 3]
-                        eout_i = Tabular(e, p, 'linear-linear', ignore_negative=True)
-                        eout_i.c = c
-
-                        # Outgoing angle distribution for each
-                        # (incoming, outgoing) energy pair
-                        mu_i = []
-                        for j in range(n_energy_out[i]):
-                            mu = ace.xss[idx + 4:idx + 4 + n_mu]
-                            p_mu = 1. / n_mu * np.ones(n_mu)
-                            mu_ij = Discrete(mu, p_mu)
-                            mu_ij.c = np.cumsum(p_mu)
-                            mu_i.append(mu_ij)
-                            idx += 3 + n_mu
-
-                        energy_out.append(eout_i)
-                        mu_out.append(mu_i)
-
-                    # Create correlated angle-energy distribution
-                    breakpoints = [n_energy]
-                    interpolation = [2]
-                    energy = self.inelastic_xs[strT].x
-                    self.inelastic_dist[strT] = CorrelatedAngleEnergy(
-                        breakpoints, interpolation, energy, energy_out, mu_out)
-
-                # Incoherent/coherent elastic scattering cross section
-                idx = ace.jxs[4]
-                if idx != 0:
-                    n_energy = int(ace.xss[idx])
-                    energy = ace.xss[idx + 1: idx + 1 + n_energy]
-                    P = ace.xss[idx + 1 + n_energy: idx + 1 + 2 * n_energy]
-
-                    if ace.nxs[5] == 4:
-                        self.elastic_xs[strT] = CoherentElastic(energy, P)
-                    else:
-                        self.elastic_xs[strT] = Tabulated1D(energy, P)
-
-                    # Angular distribution
-                    n_mu = ace.nxs[6]
-                    if n_mu != -1:
-                        idx = ace.jxs[6]
-                        self.elastic_mu_out[strT] = \
-                            ace.xss[idx:idx + n_energy * n_mu]
-                        self.elastic_mu_out[strT].shape = \
-                            (n_energy, n_mu)
-
-            else:
-                raise ValueError('Data provided for an incorrect library')
-        else:
-            raise Warning('Temperature data set already within '
-                          'IncidentNeutron object')
+        # Add elastic cross sectoin and angular distribution
+        if strT in data.elastic_xs:
+            self.elastic_xs[strT] = data.elastic_xs[strT]
+        if strT in data.elastic_mu_out:
+            self.elastic_mu_out[strT] = data.elastic_mu_out[strT]
 
     @classmethod
     def from_hdf5(cls, group_or_filename):
diff --git a/openmc/geometry.py b/openmc/geometry.py
index 6d9330dc6..4d822cc8e 100644
--- a/openmc/geometry.py
+++ b/openmc/geometry.py
@@ -65,10 +65,14 @@ class Geometry(object):
                     cell.add_volume_information(volume_calc)
 
     def export_to_xml(self, path='geometry.xml'):
-        """Create a geometry.xml file that can be used for a simulation.
+        """Export geometry to an XML file.
+
+        Parameters
+        ----------
+        path : str
+            Path to file to write. Defaults to 'geometry.xml'.
 
         """
-
         # Clear OpenMC written IDs used to optimize XML generation
         openmc.universe.WRITTEN_IDS = {}
 
diff --git a/openmc/material.py b/openmc/material.py
index 2861d5be6..0639db562 100644
--- a/openmc/material.py
+++ b/openmc/material.py
@@ -812,7 +812,12 @@ class Materials(cv.CheckedList):
             self._materials_file.append(xml_element)
 
     def export_to_xml(self, path='materials.xml'):
-        """Create a materials.xml file that can be used for a simulation.
+        """Export material collection to an XML file.
+
+        Parameters
+        ----------
+        path : str
+            Path to file to write. Defaults to 'materials.xml'.
 
         """
 
diff --git a/openmc/plots.py b/openmc/plots.py
index f5c1f2b2a..6915604a3 100644
--- a/openmc/plots.py
+++ b/openmc/plots.py
@@ -617,10 +617,14 @@ class Plots(cv.CheckedList):
             self._plots_file.append(xml_element)
 
     def export_to_xml(self, path='plots.xml'):
-        """Create a plots.xml file that can be used by OpenMC.
+        """Export plot specifications to an XML file.
+
+        Parameters
+        ----------
+        path : str
+            Path to file to write. Defaults to 'plots.xml'.
 
         """
-
         # Reset xml element tree
         self._plots_file.clear()
 
diff --git a/openmc/settings.py b/openmc/settings.py
index 15898a2b8..f255a8bb1 100644
--- a/openmc/settings.py
+++ b/openmc/settings.py
@@ -15,8 +15,7 @@ if sys.version_info[0] >= 3:
 
 
 class Settings(object):
-    """Settings file used for an OpenMC simulation. Corresponds directly to the
-    settings.xml input file.
+    """Settings used for an OpenMC simulation.
 
     Attributes
     ----------
@@ -1119,7 +1118,12 @@ class Settings(object):
                 elem.append(r.to_xml_element())
 
     def export_to_xml(self, path='settings.xml'):
-        """Create a settings.xml file that can be used for a simulation.
+        """Export simulation settings to an XML file.
+
+        Parameters
+        ----------
+        path : str
+            Path to file to write. Defaults to 'settings.xml'.
 
         """
 
diff --git a/openmc/statepoint.py b/openmc/statepoint.py
index c32f2c2d9..adc4247c4 100644
--- a/openmc/statepoint.py
+++ b/openmc/statepoint.py
@@ -676,19 +676,10 @@ class StatePoint(object):
             raise ValueError(msg)
 
         for tally_id, tally in self.tallies.items():
-            summary_tally = summary.tallies[tally_id]
-            tally.name = summary_tally.name
+            tally.name = summary.tally_names[tally_id]
             tally.with_summary = True
 
             for tally_filter in tally.filters:
-                summary_filter = summary_tally.find_filter(type(tally_filter))
-
-                if isinstance(tally_filter, openmc.SurfaceFilter):
-                    surface_ids = []
-                    for bin in tally_filter.bins:
-                        surface_ids.append(bin)
-                    tally_filter.bins = surface_ids
-
                 if isinstance(tally_filter, (openmc.CellFilter,
                                              openmc.DistribcellFilter)):
                     distribcell_ids = []
@@ -697,8 +688,9 @@ class StatePoint(object):
                     tally_filter.bins = distribcell_ids
 
                 if isinstance(tally_filter, (openmc.DistribcellFilter)):
-                    tally_filter.distribcell_paths = \
-                        summary_filter.distribcell_paths
+                    cell_id = tally_filter.bins[0]
+                    cell = summary.get_cell_by_id(cell_id)
+                    tally_filter.distribcell_paths = cell.distribcell_paths
 
                 if isinstance(tally_filter, openmc.UniverseFilter):
                     universe_ids = []
diff --git a/openmc/summary.py b/openmc/summary.py
index a5862bb04..fd1689eb2 100644
--- a/openmc/summary.py
+++ b/openmc/summary.py
@@ -297,10 +297,13 @@ class Summary(object):
                 cell.region = Region.from_expression(
                     region, {s.id: s for s in self.surfaces.values()})
 
-            # Get the distribcell index
-            ind = self._f['geometry/cells'][key]['distribcell_index'].value
-            if ind != 0:
+            # Get the distribcell data
+            if 'distribcell_index' in self._f['geometry/cells'][key]:
+                ind = self._f['geometry/cells'][key]['distribcell_index'].value
                 cell.distribcell_index = ind
+                paths = self._f['geometry/cells'][key]['paths'][...]
+                paths = [str(path.decode()) for path in paths]
+                cell.distribcell_paths = paths
 
             # Add the Cell to the global dictionary of all Cells
             self.cells[index] = cell
@@ -520,18 +523,15 @@ class Summary(object):
         self.openmc_geometry.root_universe = root_universe
 
     def _read_tallies(self):
-        # Initialize dictionaries for the Tallies
+        # Initialize a dictionary for the tally names
         # Keys     - Tally IDs
-        # Values   - Tally objects
-        self.tallies = {}
+        # Values   - Tally names
+        self.tally_names = {}
 
         # Read the number of tallies
         if 'tallies' not in self._f:
-            self.n_tallies = 0
             return
 
-        self.n_tallies = self._f['tallies/n_tallies'].value
-
         # OpenMC Tally keys
         all_keys = self._f['tallies/'].keys()
         tally_keys = [key for key in all_keys if 'tally' in key]
@@ -545,34 +545,7 @@ class Summary(object):
 
             # Read Tally name metadata
             tally_name = self._f['{0}/name'.format(subbase)].value.decode()
-
-            # Create Tally object and assign basic properties
-            tally = openmc.Tally(tally_id, tally_name)
-
-            # Read scattering moment order strings (e.g., P3, Y1,2, etc.)
-            moments = self._f['{0}/moment_orders'.format(subbase)].value
-
-            # Read score metadata
-            scores = self._f['{0}/score_bins'.format(subbase)].value
-            for j, score in enumerate(scores):
-                score = score.decode()
-
-                # If this is a moment, use generic moment order
-                pattern = r'-n$|-pn$|-yn$'
-                score = re.sub(pattern, '-' + moments[j].decode(), score)
-                tally.scores.append(score)
-
-            # Read filter metadata
-            num_filters = self._f['{0}/n_filters'.format(subbase)].value
-
-            # Read all filters
-            for j in range(1, num_filters+1):
-                subsubbase = '{0}/filter {1}'.format(subbase, j)
-                new_filter = openmc.Filter.from_hdf5(self._f[subsubbase])
-                tally.filters.append(new_filter)
-
-            # Add Tally to the global dictionary of all Tallies
-            self.tallies[tally_id] = tally
+            self.tally_names[tally_id] = tally_name
 
     def add_volume_information(self, volume_calc):
         """Add volume information to the geometry within the summary file
diff --git a/scripts/openmc-ace-to-hdf5 b/scripts/openmc-ace-to-hdf5
index ff8c19962..dd115d175 100755
--- a/scripts/openmc-ace-to-hdf5
+++ b/scripts/openmc-ace-to-hdf5
@@ -190,7 +190,7 @@ for filename in ace_libraries:
                 thermal.export_to_hdf5(outfile, 'w')
 
                 # Register with library
-                library.register_file(outfile, 'thermal')
+                library.register_file(outfile)
 
                 # Add data to list
                 nuclides[name] = outfile
diff --git a/src/constants.F90 b/src/constants.F90
index c877472c2..cc0d663ea 100644
--- a/src/constants.F90
+++ b/src/constants.F90
@@ -14,7 +14,7 @@ module constants
   integer,       parameter :: REVISION_STATEPOINT       = 15
   integer,       parameter :: REVISION_PARTICLE_RESTART = 1
   integer,       parameter :: REVISION_TRACK            = 1
-  integer,       parameter :: REVISION_SUMMARY          = 3
+  integer,       parameter :: REVISION_SUMMARY          = 4
   character(10), parameter :: MULTIPOLE_VERSION         = "v0.2"
 
   ! ============================================================================
diff --git a/src/plot.F90 b/src/plot.F90
index 796cce6af..b5832bbdf 100644
--- a/src/plot.F90
+++ b/src/plot.F90
@@ -239,10 +239,10 @@ contains
     xyz_ll_plot = pl % origin
     xyz_ur_plot = pl % origin
 
-    xyz_ll_plot(outer) = pl % origin(1) - pl % width(1) / TWO
-    xyz_ll_plot(inner) = pl % origin(2) - pl % width(2) / TWO
-    xyz_ur_plot(outer) = pl % origin(1) + pl % width(1) / TWO
-    xyz_ur_plot(inner) = pl % origin(2) + pl % width(2) / TWO
+    xyz_ll_plot(outer) = pl % origin(outer) - pl % width(1) / TWO
+    xyz_ll_plot(inner) = pl % origin(inner) - pl % width(2) / TWO
+    xyz_ur_plot(outer) = pl % origin(outer) + pl % width(1) / TWO
+    xyz_ur_plot(inner) = pl % origin(inner) + pl % width(2) / TWO
 
     width = xyz_ur_plot - xyz_ll_plot
 
diff --git a/src/summary.F90 b/src/summary.F90
index cc0c51758..97abeb206 100644
--- a/src/summary.F90
+++ b/src/summary.F90
@@ -2,8 +2,8 @@ module summary
 
   use constants
   use endf,            only: reaction_name
-  use geometry_header, only: Cell, Universe, Lattice, RectLattice, &
-                             &HexLattice
+  use geometry_header, only: BASE_UNIVERSE, Cell, Universe, Lattice, &
+                             RectLattice, HexLattice
   use global
   use hdf5_interface
   use material_header, only: Material
@@ -13,6 +13,7 @@ module summary
   use surface_header
   use string,          only: to_str
   use tally_header,    only: TallyObject
+  use tally_filter,    only: find_offset
 
   use hdf5
 
@@ -150,7 +151,7 @@ contains
   subroutine write_geometry(file_id)
     integer(HID_T), intent(in) :: file_id
 
-    integer          :: i, j, k, m
+    integer          :: i, j, k, m, offset
     integer, allocatable :: lattice_universes(:,:,:)
     integer, allocatable :: cell_materials(:)
     real(8), allocatable :: cell_temperatures(:)
@@ -161,6 +162,8 @@ contains
     integer(HID_T) :: lattices_group, lattice_group
     real(8), allocatable :: coeffs(:)
     character(REGION_SPEC_LEN) :: region_spec
+    character(MAX_LINE_LEN), allocatable :: paths(:)
+    character(MAX_LINE_LEN)              :: path
     type(Cell),     pointer :: c
     class(Surface), pointer :: s
     type(Universe), pointer :: u
@@ -266,7 +269,21 @@ contains
       end do
       call write_dataset(cell_group, "region", adjustl(region_spec))
 
-      call write_dataset(cell_group, "distribcell_index", c % distribcell_index)
+      ! Write distribcell data
+      if (c % distribcell_index /= NONE) then
+        call write_dataset(cell_group, "distribcell_index", &
+                           c % distribcell_index)
+
+        allocate(paths(c % instances))
+        do k = 1, c % instances
+          path = ''
+          offset = 1
+          call find_offset(i, universes(BASE_UNIVERSE), k, offset, path)
+          paths(k) = path
+        end do
+        call write_dataset(cell_group, "paths", paths)
+        deallocate(paths)
+      end if
 
       call close_group(cell_group)
     end do CELL_LOOP
@@ -566,119 +583,21 @@ contains
   subroutine write_tallies(file_id)
     integer(HID_T), intent(in) :: file_id
 
-    integer :: i, j, k
-    integer :: n_order      ! loop index for moment orders
-    integer :: nm_order     ! loop index for Ynm moment orders
+    integer :: i
     integer(HID_T) :: tallies_group
-    integer(HID_T) :: mesh_group
     integer(HID_T) :: tally_group
-    integer(HID_T) :: filter_group
-    character(20), allocatable :: str_array(:)
-    type(RegularMesh), pointer :: m
     type(TallyObject), pointer :: t
 
     tallies_group = create_group(file_id, "tallies")
 
-    ! Write total number of meshes
-    call write_dataset(tallies_group, "n_meshes", n_meshes)
-
-    ! Write information for meshes
-    MESH_LOOP: do i = 1, n_meshes
-      m => meshes(i)
-      mesh_group = create_group(tallies_group, "mesh " // trim(to_str(m%id)))
-
-      ! Write internal OpenMC index for this mesh
-      call write_dataset(mesh_group, "index", i)
-
-      ! Write type and number of dimensions
-      call write_dataset(mesh_group, "type", "regular")
-
-      ! Write mesh information
-      call write_dataset(mesh_group, "dimension", m%dimension)
-      call write_dataset(mesh_group, "lower_left", m%lower_left)
-      call write_dataset(mesh_group, "upper_right", m%upper_right)
-      call write_dataset(mesh_group, "width", m%width)
-
-      call close_group(mesh_group)
-    end do MESH_LOOP
-
-    ! Write number of tallies
-    call write_dataset(tallies_group, "n_tallies", n_tallies)
-
     TALLY_METADATA: do i = 1, n_tallies
       ! Get pointer to tally
       t => tallies(i)
-      tally_group = create_group(tallies_group, "tally " // trim(to_str(t%id)))
-
-      ! Write internal OpenMC index for this tally
-      call write_dataset(tally_group, "index", i)
+      tally_group = create_group(tallies_group, "tally " &
+           // trim(to_str(t % id)))
 
       ! Write the name for this tally
-      call write_dataset(tally_group, "name", t%name)
-
-      ! Write number of filters
-      call write_dataset(tally_group, "n_filters", size(t % filters))
-
-      FILTER_LOOP: do j = 1, size(t % filters)
-        filter_group = create_group(tally_group, "filter " // trim(to_str(j)))
-        call t % filters(j) % obj % to_summary(filter_group)
-        call close_group(filter_group)
-      end do FILTER_LOOP
-
-      ! Create temporary array for nuclide bins
-      allocate(str_array(t%n_nuclide_bins))
-      NUCLIDE_LOOP: do j = 1, t%n_nuclide_bins
-        if (t%nuclide_bins(j) > 0) then
-          str_array(j) = nuclides(t % nuclide_bins(j)) % name
-        else
-          str_array(j) = 'total'
-        end if
-      end do NUCLIDE_LOOP
-
-      ! Write and deallocate nuclide bins
-      call write_dataset(tally_group, "nuclides", str_array)
-      deallocate(str_array)
-
-      ! Write number of score bins
-      call write_dataset(tally_group, "n_score_bins", t%n_score_bins)
-      allocate(str_array(size(t%score_bins)))
-      do j = 1, size(t%score_bins)
-        str_array(j) = reaction_name(t%score_bins(j))
-      end do
-      call write_dataset(tally_group, "score_bins", str_array)
-
-      deallocate(str_array)
-
-      ! Write explicit moment order strings for each score bin
-      k = 1
-      allocate(str_array(t%n_score_bins))
-      MOMENT_LOOP: do j = 1, t%n_user_score_bins
-        select case(t%score_bins(k))
-        case (SCORE_SCATTER_N, SCORE_NU_SCATTER_N)
-          str_array(k) = 'P' // trim(to_str(t%moment_order(k)))
-          k = k + 1
-        case (SCORE_SCATTER_PN, SCORE_NU_SCATTER_PN)
-          do n_order = 0, t%moment_order(k)
-            str_array(k) = 'P' // trim(to_str(n_order))
-            k = k + 1
-          end do
-        case (SCORE_SCATTER_YN, SCORE_NU_SCATTER_YN, SCORE_FLUX_YN, &
-             SCORE_TOTAL_YN)
-          do n_order = 0, t%moment_order(k)
-            do nm_order = -n_order, n_order
-              str_array(k) = 'Y' // trim(to_str(n_order)) // ',' // &
-                   trim(to_str(nm_order))
-              k = k + 1
-            end do
-          end do
-        case default
-          str_array(k) = ''
-          k = k + 1
-        end select
-      end do MOMENT_LOOP
-
-      call write_dataset(tally_group, "moment_orders", str_array)
-      deallocate(str_array)
+      call write_dataset(tally_group, "name", t % name)
 
       call close_group(tally_group)
     end do TALLY_METADATA
diff --git a/src/tally_filter.F90 b/src/tally_filter.F90
index 51b93ac4c..f15120346 100644
--- a/src/tally_filter.F90
+++ b/src/tally_filter.F90
@@ -80,7 +80,6 @@ module tally_filter
   contains
     procedure :: get_next_bin => get_next_bin_distribcell
     procedure :: to_statepoint => to_statepoint_distribcell
-    procedure :: to_summary => to_summary_distribcell
     procedure :: text_label => text_label_distribcell
     procedure :: initialize => initialize_distribcell
   end type DistribcellFilter
@@ -714,36 +713,6 @@ contains
     call write_dataset(filter_group, "bins", this % cell )
   end subroutine to_statepoint_distribcell
 
-  subroutine to_summary_distribcell(this, filter_group)
-    class(DistribcellFilter), intent(in) :: this
-    integer(HID_T),           intent(in) :: filter_group
-
-    integer                              :: offset, k
-    character(MAX_LINE_LEN), allocatable :: paths(:)
-    character(MAX_LINE_LEN)              :: path
-
-    call write_dataset(filter_group, "type", "distribcell")
-    call write_dataset(filter_group, "n_bins", this % n_bins)
-    call write_dataset(filter_group, "bins", this % cell )
-
-    ! Write paths to reach each distribcell instance
-
-    ! Allocate array of strings for each distribcell path
-    allocate(paths(this % n_bins))
-
-    ! Store path for each distribcell instance
-    do k = 1, this % n_bins
-      path = ''
-      offset = 1
-      call find_offset(this % cell, universes(BASE_UNIVERSE), k, offset, path)
-      paths(k) = path
-    end do
-
-    ! Write array of distribcell paths to summary file
-    call write_dataset(filter_group, "paths", paths)
-    deallocate(paths)
-  end subroutine to_summary_distribcell
-
   subroutine initialize_distribcell(this)
     class(DistribcellFilter), intent(inout) :: this
 
diff --git a/src/tally_filter_header.F90 b/src/tally_filter_header.F90
index 43ef846e3..8541cd334 100644
--- a/src/tally_filter_header.F90
+++ b/src/tally_filter_header.F90
@@ -18,7 +18,6 @@ module tally_filter_header
   contains
     procedure(get_next_bin_),  deferred :: get_next_bin
     procedure(to_statepoint_), deferred :: to_statepoint
-    procedure                           :: to_summary => filter_to_summary
     procedure(text_label_),    deferred :: text_label
     procedure                           :: initialize => filter_initialize
   end type TallyFilter
@@ -82,18 +81,6 @@ module tally_filter_header
 
 contains
 
-!===============================================================================
-! TO_SUMMARY writes all the information needed to reconstruct the filter to the
-! given filter_group.  If this procedure is not overridden by the derived class,
-! then it will call to_statepoint by default.
-
-  subroutine filter_to_summary(this, filter_group)
-    class(TallyFilter), intent(in) :: this
-    integer(HID_T),     intent(in) :: filter_group
-
-    call this % to_statepoint(filter_group)
-  end subroutine filter_to_summary
-
 !===============================================================================
 ! INITIALIZE sets up any internal data, as necessary.  If this procedure is not
 ! overriden by the derived class, then it will do nothing by default.