diff --git a/docs/source/pythonapi/examples/mgxs-part-ii.ipynb b/docs/source/pythonapi/examples/mgxs-part-ii.ipynb
index 5e48244b7..359fd4eaa 100644
--- a/docs/source/pythonapi/examples/mgxs-part-ii.ipynb
+++ b/docs/source/pythonapi/examples/mgxs-part-ii.ipynb
@@ -332,7 +332,7 @@
    "outputs": [],
    "source": [
     "# Extract all Cells filled by Materials\n",
-    "openmc_cells = openmc_geometry.get_all_material_cells()\n",
+    "openmc_cells = openmc_geometry.get_all_material_cells().values()\n",
     "\n",
     "# Create dictionary to store multi-group cross sections for all cells\n",
     "xs_library = {}\n",
diff --git a/docs/source/pythonapi/examples/mgxs-part-iii.ipynb b/docs/source/pythonapi/examples/mgxs-part-iii.ipynb
index b76696d7f..69297d884 100644
--- a/docs/source/pythonapi/examples/mgxs-part-iii.ipynb
+++ b/docs/source/pythonapi/examples/mgxs-part-iii.ipynb
@@ -594,7 +594,7 @@
     "mgxs_lib.domain_type = 'cell'\n",
     "\n",
     "# Specify the cell domains over which to compute multi-group cross sections\n",
-    "mgxs_lib.domains = geometry.get_all_material_cells()"
+    "mgxs_lib.domains = geometry.get_all_material_cells().values()"
    ]
   },
   {
@@ -743,8 +743,8 @@
       "         Copyright | 2011-2017 Massachusetts Institute of Technology\n",
       "           License | http://openmc.readthedocs.io/en/latest/license.html\n",
       "           Version | 0.8.0\n",
-      "          Git SHA1 | dfc6f1d9bf1b31fc94dad1cb30bd672b25f47e04\n",
-      "         Date/Time | 2017-02-21 15:11:55\n",
+      "          Git SHA1 | 7ca46e809ce01fe7857ae36072822a1718f01aaf\n",
+      "         Date/Time | 2017-02-23 09:36:51\n",
       "    OpenMP Threads | 4\n",
       "\n",
       " ===========================================================================\n",
@@ -831,20 +831,20 @@
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
-      " Total time for initialization     =  5.2287E-01 seconds\n",
-      "   Reading cross sections          =  3.5267E-01 seconds\n",
-      " Total time in simulation          =  1.3331E+01 seconds\n",
-      "   Time in transport only          =  1.3109E+01 seconds\n",
-      "   Time in inactive batches        =  9.4551E-01 seconds\n",
-      "   Time in active batches          =  1.2385E+01 seconds\n",
-      "   Time synchronizing fission bank =  3.8467E-03 seconds\n",
-      "     Sampling source sites         =  2.5076E-03 seconds\n",
-      "     SEND/RECV source sites        =  1.2758E-03 seconds\n",
-      "   Time accumulating tallies       =  7.0302E-04 seconds\n",
-      " Total time for finalization       =  1.5000E-05 seconds\n",
-      " Total time elapsed                =  1.3863E+01 seconds\n",
-      " Calculation Rate (inactive)       =  26440.9 neutrons/second\n",
-      " Calculation Rate (active)         =  8074.24 neutrons/second\n",
+      " Total time for initialization     =  4.2238E-01 seconds\n",
+      "   Reading cross sections          =  3.1986E-01 seconds\n",
+      " Total time in simulation          =  1.3628E+01 seconds\n",
+      "   Time in transport only          =  1.3342E+01 seconds\n",
+      "   Time in inactive batches        =  1.1012E+00 seconds\n",
+      "   Time in active batches          =  1.2527E+01 seconds\n",
+      "   Time synchronizing fission bank =  3.7750E-03 seconds\n",
+      "     Sampling source sites         =  2.4760E-03 seconds\n",
+      "     SEND/RECV source sites        =  1.2253E-03 seconds\n",
+      "   Time accumulating tallies       =  6.5502E-04 seconds\n",
+      " Total time for finalization       =  9.4890E-06 seconds\n",
+      " Total time elapsed                =  1.4059E+01 seconds\n",
+      " Calculation Rate (inactive)       =  22702.0 neutrons/second\n",
+      " Calculation Rate (active)         =  7982.70 neutrons/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
@@ -1663,6 +1663,7 @@
   }
  ],
  "metadata": {
+  "anaconda-cloud": {},
   "kernelspec": {
    "display_name": "Python [default]",
    "language": "python",
diff --git a/docs/source/pythonapi/examples/mgxs-part-iv.ipynb b/docs/source/pythonapi/examples/mgxs-part-iv.ipynb
index 125340af7..dfab3b2be 100644
--- a/docs/source/pythonapi/examples/mgxs-part-iv.ipynb
+++ b/docs/source/pythonapi/examples/mgxs-part-iv.ipynb
@@ -425,7 +425,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN\nAAB6JgAAgIQAAPoAAACA6AAAdTAAAOpgAAA6mAAAF3CculE8AAAADFBMVEX////pgJFyEhJNv8RV\nUZDeAAAAAWJLR0QAiAUdSAAAAAd0SU1FB+ECCw4MBB1LEPkAAAWFSURBVGje7Zs7cttADIZ9CSvX\ncrP0iCxUqbBc8Ag6xR6BhV2EvYvwFD4CCx1ABT1jMdgndpegRQnOrCbjpPlGESISC4A/gd27e8H5\n83CX3b4+iKJrRHkS4vkghMPBonRYWGwtfgD2YN+dRDUOoh6lACw0Noi9w2fESuEoAR/uVuMolX03\n9oXGT7F3eFL2iEfhUX1f4cPdL/ishs+68ai+udE4xPhexbjX2FfjGNoPj/DPNX4Tsd+EODr8FvsV\ndf1Hd9P2VvCi4+s/aXvrf+upAD+1/9GV1mkOH5X9vV6THtfvACslcaUCbESL61drBPtdI8SrFMWr\nELsXCkuFDYW75gbiP7d9Cf7bAYI/aCwUShrBvh30+lWQkzVgZ/HD4OixNCgcQpJ3BxU/Ln91elKo\nM5VEE38QtJ+Yv6cQ9xjKNYayyl8TypP8DfJnQ2H/b/N3ye9P83cT33SQv/sQh9gV7zZ/0dNj5HQa\nC5vVzv9+/WFN2w8KVaZ2BwL1+pv4g0x1QRfjq0dB4Q3kT277oP6VNL6gKxNU9a8zK+WLbi/Wwpdi\nhbboKqyxFOulHMj6v4W/AXbmUeAxrv9J/CqEBXaRKsXaodD4nsYvkT/G6H1D4SR/iPy1Roj9JsQ5\ne18/7EUHv1+Fvx/Xj5V9Ugb5K8TW4TZEEdcvoz/up0VTe9qsVIppKVX6a7D6y9ZvwEKjrtQxPtv6\nfXII9vCxKOGaIeAIfEF8IvAG8ie3vRK9rRQl+PPpSctbhfpTUCpviH+kxsZgpT91+snoX1l49KK3\niUQvICRy5aUw6l8leoVwoo3Uv1rKreF/UFLY6d9QP4L9Wf2r7EP9GOSfcsjZ56f60kz+XmVPXv+R\nuP49ff0T/53Rv6n/7m2lvXT9Wqd/VUz8hvh5M/ED6ILmt4mfHYZSaePnTWpsf/SvqV9O6dLYYClL\nEetnoH/LBLFoBvrX189uTv8++kot5vTvQD4/9jP690g9P/4z/bvo/XVG/xYoZZx+8fr3MxAtsf7t\nUOkG2JqsTtCIpgCt/qX1226KqZS7gfzJbe+c9jLrtIZ8lXD+s4umlW6AKIVrlML2/cXjgPFjlJqI\nRC+Fj0bVJe+vSh56pSdR6YkQ1ygF10Wqf0FeLta/iKn9Mv1L24ti2e+7W4n1b3T/W+L+t9H9T/Sv\nVboUmqJJon1/hZq8LnzRDlDrX1u0xRT1+6vEpomMmyYkqi95vIH8yW1PN+122KkLcNLKi/WTF01z\n/cNASrWE/l3ev6T17zX909z9X27/euK/Rf3zWP+Waf9eEv37KkWJ+rfDl6ZglNDa+cEBhwYDvkoN\nP/rX69814NaI3imq0l7OYDy/qSdDGwr7r+Y3VbzoKZr6XX2lfxfOb87qXzr+b1j/Xlp/nP6dn98M\ncdH7cn7zjPObKsYWS3Eb9w8n85smHtqQuPuZ30T2dlIT6F9xFl+n8xslegL9a4c2KRr9W4rp/GYq\numiM9Nec/j2v/yj9u1h//hv9e93vc++f63/u+rPjL3f+5Lbn1j9m/eXWf+7zh/v8+2b9e/Hzn6s/\nuPqHrb8g71n6L3f+5Lbnvn8w33+4718/+5d47//c/gO7/5E7/nPbc/tv3P4fs//I7X9y+6/fqH+v\n6j9z+9/c/ju3/8+eP+TOn9z23PkXc/7Gnf9x5483q38Xzn+582fu/Js9fy8kb/6fO39y23P3n3S8\n/S/c/Tfc/T83uX/pgv1XE/9duP+Lu/+Mvf8td/znti8kb/8ld/9nx9t/Sjw/Ltr/yt1/+337f6/b\nf0zoB3nJ/ucVc/81d/83e/957vzJbc89/8A8f8E9/5HE78XnT/4H/cs5f8Q9/8Q9f8U+/5U7f3Lb\nc88fdrzzjyvm+cuf/Uu887/c88fs88954/8vO4SjPC+2QRIAAAAldEVYdGRhdGU6Y3JlYXRlADIw\nMTctMDItMTFUMTQ6MTI6MDQtMDU6MDDP346NAAAAJXRFWHRkYXRlOm1vZGlmeQAyMDE3LTAyLTEx\nVDE0OjEyOjA0LTA1OjAwvoI2MQAAAABJRU5ErkJggg==\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN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\nVDA5OjM5OjEwLTA2OjAwOkcAiwAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -529,7 +529,7 @@
     "mgxs_lib.domain_type = \"material\"\n",
     "\n",
     "# Specify the cell domains over which to compute multi-group cross sections\n",
-    "mgxs_lib.domains = geometry.get_all_materials()"
+    "mgxs_lib.domains = geometry.get_all_materials().values()"
    ]
   },
   {
@@ -571,7 +571,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/nelsonag/git/openmc/openmc/mgxs/library.py:426: RuntimeWarning: The P0 correction will be ignored since the scattering order 0 is greater than zero\n",
+      "/home/romano/openmc/openmc/mgxs/library.py:412: RuntimeWarning: The P0 correction will be ignored since the scattering order 0 is greater than zero\n",
       "  warn(msg, RuntimeWarning)\n"
      ]
     }
@@ -727,9 +727,9 @@
       "         Copyright | 2011-2017 Massachusetts Institute of Technology\n",
       "           License | http://openmc.readthedocs.io/en/latest/license.html\n",
       "           Version | 0.8.0\n",
-      "          Git SHA1 | 6e3f6bf8b11cb3f6f171f1c351ddcaf85dd515ec\n",
-      "         Date/Time | 2017-02-11 14:12:04\n",
-      "    OpenMP Threads | 8\n",
+      "          Git SHA1 | 7ca46e809ce01fe7857ae36072822a1718f01aaf\n",
+      "         Date/Time | 2017-02-23 09:39:10\n",
+      "    OpenMP Threads | 4\n",
       "\n",
       " ===========================================================================\n",
       " ========================>     INITIALIZATION     <=========================\n",
@@ -739,12 +739,12 @@
       " Reading geometry XML file...\n",
       " Reading materials XML file...\n",
       " Reading cross sections XML file...\n",
-      " Reading U235 from /opt/xsdata/nndc/U235.h5\n",
-      " Reading U238 from /opt/xsdata/nndc/U238.h5\n",
-      " Reading O16 from /opt/xsdata/nndc/O16.h5\n",
-      " Reading Zr90 from /opt/xsdata/nndc/Zr90.h5\n",
-      " Reading H1 from /opt/xsdata/nndc/H1.h5\n",
-      " Reading B10 from /opt/xsdata/nndc/B10.h5\n",
+      " Reading U235 from /home/romano/openmc/scripts/nndc_hdf5/U235.h5\n",
+      " Reading U238 from /home/romano/openmc/scripts/nndc_hdf5/U238.h5\n",
+      " Reading O16 from /home/romano/openmc/scripts/nndc_hdf5/O16.h5\n",
+      " Reading Zr90 from /home/romano/openmc/scripts/nndc_hdf5/Zr90.h5\n",
+      " Reading H1 from /home/romano/openmc/scripts/nndc_hdf5/H1.h5\n",
+      " Reading B10 from /home/romano/openmc/scripts/nndc_hdf5/B10.h5\n",
       " Maximum neutron transport energy: 2.00000E+07 eV for U235\n",
       " Reading tallies XML file...\n",
       " Building neighboring cells lists for each surface...\n",
@@ -756,56 +756,56 @@
       "\n",
       "  Bat./Gen.      k            Average k         \n",
       "  =========   ========   ====================   \n",
-      "        1/1    1.05201                       \n",
-      "        2/1    1.02017                       \n",
-      "        3/1    1.02398                       \n",
-      "        4/1    1.02677                       \n",
-      "        5/1    1.01070                       \n",
-      "        6/1    1.02964                       \n",
-      "        7/1    1.02163                       \n",
-      "        8/1    1.04524                       \n",
-      "        9/1    1.00773                       \n",
-      "       10/1    1.01536                       \n",
-      "       11/1    1.02992                       \n",
-      "       12/1    1.03248    1.03120 +/- 0.00128\n",
-      "       13/1    0.99044    1.01761 +/- 0.01361\n",
-      "       14/1    1.01484    1.01692 +/- 0.00965\n",
-      "       15/1    1.01491    1.01652 +/- 0.00748\n",
-      "       16/1    1.03809    1.02011 +/- 0.00709\n",
-      "       17/1    1.02536    1.02086 +/- 0.00604\n",
-      "       18/1    1.03663    1.02283 +/- 0.00559\n",
-      "       19/1    1.03902    1.02463 +/- 0.00525\n",
-      "       20/1    1.01557    1.02373 +/- 0.00478\n",
-      "       21/1    1.01286    1.02274 +/- 0.00443\n",
-      "       22/1    1.01392    1.02200 +/- 0.00411\n",
-      "       23/1    1.04439    1.02372 +/- 0.00416\n",
-      "       24/1    1.04034    1.02491 +/- 0.00403\n",
-      "       25/1    0.99433    1.02287 +/- 0.00427\n",
-      "       26/1    1.02720    1.02314 +/- 0.00400\n",
-      "       27/1    1.03545    1.02387 +/- 0.00383\n",
-      "       28/1    1.03853    1.02468 +/- 0.00370\n",
-      "       29/1    1.02735    1.02482 +/- 0.00350\n",
-      "       30/1    1.02429    1.02480 +/- 0.00332\n",
-      "       31/1    1.02901    1.02500 +/- 0.00317\n",
-      "       32/1    1.03296    1.02536 +/- 0.00304\n",
-      "       33/1    1.03605    1.02582 +/- 0.00294\n",
-      "       34/1    1.04247    1.02652 +/- 0.00290\n",
-      "       35/1    1.02088    1.02629 +/- 0.00279\n",
-      "       36/1    1.03017    1.02644 +/- 0.00269\n",
-      "       37/1    1.03216    1.02665 +/- 0.00259\n",
-      "       38/1    1.01459    1.02622 +/- 0.00254\n",
-      "       39/1    1.03706    1.02659 +/- 0.00248\n",
-      "       40/1    1.01383    1.02617 +/- 0.00243\n",
-      "       41/1    0.99028    1.02501 +/- 0.00262\n",
-      "       42/1    1.02969    1.02516 +/- 0.00254\n",
-      "       43/1    1.02097    1.02503 +/- 0.00247\n",
-      "       44/1    1.02650    1.02507 +/- 0.00239\n",
-      "       45/1    1.03939    1.02548 +/- 0.00236\n",
-      "       46/1    1.00974    1.02505 +/- 0.00233\n",
-      "       47/1    1.02563    1.02506 +/- 0.00227\n",
-      "       48/1    1.00377    1.02450 +/- 0.00228\n",
-      "       49/1    0.99082    1.02364 +/- 0.00238\n",
-      "       50/1    1.00805    1.02325 +/- 0.00235\n",
+      "        1/1    1.05169                       \n",
+      "        2/1    1.02178                       \n",
+      "        3/1    1.02778                       \n",
+      "        4/1    1.01814                       \n",
+      "        5/1    1.02993                       \n",
+      "        6/1    1.05836                       \n",
+      "        7/1    1.00011                       \n",
+      "        8/1    1.04961                       \n",
+      "        9/1    1.00908                       \n",
+      "       10/1    1.00134                       \n",
+      "       11/1    1.04275                       \n",
+      "       12/1    1.04456    1.04366 +/- 0.00090\n",
+      "       13/1    1.00699    1.03143 +/- 0.01224\n",
+      "       14/1    1.05509    1.03735 +/- 0.01048\n",
+      "       15/1    1.01645    1.03317 +/- 0.00913\n",
+      "       16/1    1.02813    1.03233 +/- 0.00750\n",
+      "       17/1    1.03686    1.03298 +/- 0.00637\n",
+      "       18/1    1.04389    1.03434 +/- 0.00569\n",
+      "       19/1    1.01404    1.03208 +/- 0.00550\n",
+      "       20/1    1.01838    1.03071 +/- 0.00511\n",
+      "       21/1    1.02745    1.03042 +/- 0.00463\n",
+      "       22/1    1.03013    1.03039 +/- 0.00422\n",
+      "       23/1    1.02808    1.03022 +/- 0.00389\n",
+      "       24/1    1.04615    1.03135 +/- 0.00378\n",
+      "       25/1    1.03395    1.03153 +/- 0.00352\n",
+      "       26/1    1.05256    1.03284 +/- 0.00355\n",
+      "       27/1    1.03201    1.03279 +/- 0.00333\n",
+      "       28/1    1.00136    1.03105 +/- 0.00359\n",
+      "       29/1    1.01409    1.03015 +/- 0.00351\n",
+      "       30/1    1.01159    1.02923 +/- 0.00346\n",
+      "       31/1    1.02533    1.02904 +/- 0.00330\n",
+      "       32/1    1.04169    1.02962 +/- 0.00320\n",
+      "       33/1    1.02904    1.02959 +/- 0.00305\n",
+      "       34/1    1.04991    1.03044 +/- 0.00304\n",
+      "       35/1    1.03223    1.03051 +/- 0.00292\n",
+      "       36/1    1.02835    1.03043 +/- 0.00281\n",
+      "       37/1    1.02690    1.03029 +/- 0.00270\n",
+      "       38/1    1.02993    1.03028 +/- 0.00261\n",
+      "       39/1    1.03601    1.03048 +/- 0.00252\n",
+      "       40/1    1.04026    1.03081 +/- 0.00246\n",
+      "       41/1    1.00972    1.03013 +/- 0.00247\n",
+      "       42/1    1.04266    1.03052 +/- 0.00243\n",
+      "       43/1    1.01825    1.03015 +/- 0.00238\n",
+      "       44/1    1.05496    1.03087 +/- 0.00242\n",
+      "       45/1    1.01621    1.03046 +/- 0.00239\n",
+      "       46/1    1.04001    1.03072 +/- 0.00234\n",
+      "       47/1    1.04887    1.03121 +/- 0.00233\n",
+      "       48/1    1.02937    1.03116 +/- 0.00226\n",
+      "       49/1    1.01104    1.03065 +/- 0.00226\n",
+      "       50/1    1.03586    1.03078 +/- 0.00221\n",
       " Creating state point statepoint.50.h5...\n",
       "\n",
       " ===========================================================================\n",
@@ -815,27 +815,27 @@
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
-      " Total time for initialization     =  3.6381E-01 seconds\n",
-      "   Reading cross sections          =  2.7394E-01 seconds\n",
-      " Total time in simulation          =  8.2977E+00 seconds\n",
-      "   Time in transport only          =  8.2033E+00 seconds\n",
-      "   Time in inactive batches        =  1.0881E+00 seconds\n",
-      "   Time in active batches          =  7.2096E+00 seconds\n",
-      "   Time synchronizing fission bank =  5.2924E-03 seconds\n",
-      "     Sampling source sites         =  3.6536E-03 seconds\n",
-      "     SEND/RECV source sites        =  1.6013E-03 seconds\n",
-      "   Time accumulating tallies       =  1.1932E-04 seconds\n",
-      " Total time for finalization       =  3.3830E-06 seconds\n",
-      " Total time elapsed                =  8.6802E+00 seconds\n",
-      " Calculation Rate (inactive)       =  45950.7 neutrons/second\n",
-      " Calculation Rate (active)         =  27740.7 neutrons/second\n",
+      " Total time for initialization     =  4.2488E-01 seconds\n",
+      "   Reading cross sections          =  3.1421E-01 seconds\n",
+      " Total time in simulation          =  1.7708E+01 seconds\n",
+      "   Time in transport only          =  1.7283E+01 seconds\n",
+      "   Time in inactive batches        =  1.7937E+00 seconds\n",
+      "   Time in active batches          =  1.5915E+01 seconds\n",
+      "   Time synchronizing fission bank =  5.8977E-03 seconds\n",
+      "     Sampling source sites         =  3.9998E-03 seconds\n",
+      "     SEND/RECV source sites        =  1.8525E-03 seconds\n",
+      "   Time accumulating tallies       =  1.4703E-04 seconds\n",
+      " Total time for finalization       =  4.6920E-06 seconds\n",
+      " Total time elapsed                =  1.8150E+01 seconds\n",
+      " Calculation Rate (inactive)       =  27874.7 neutrons/second\n",
+      " Calculation Rate (active)         =  12567.0 neutrons/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
-      " k-effective (Collision)     =  1.02354 +/-  0.00215\n",
-      " k-effective (Track-length)  =  1.02325 +/-  0.00235\n",
-      " k-effective (Absorption)    =  1.02582 +/-  0.00193\n",
-      " Combined k-effective        =  1.02474 +/-  0.00151\n",
+      " k-effective (Collision)     =  1.02955 +/-  0.00250\n",
+      " k-effective (Track-length)  =  1.03078 +/-  0.00221\n",
+      " k-effective (Absorption)    =  1.02896 +/-  0.00244\n",
+      " Combined k-effective        =  1.03019 +/-  0.00179\n",
       " Leakage Fraction            =  0.00000 +/-  0.00000\n",
       "\n"
      ]
@@ -971,11 +971,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/nelsonag/git/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/home/romano/openmc/openmc/tallies.py:1835: RuntimeWarning: invalid value encountered in true_divide\n",
       "  self_rel_err = data['self']['std. dev.'] / data['self']['mean']\n",
-      "/home/nelsonag/git/openmc/openmc/tallies.py:1836: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/home/romano/openmc/openmc/tallies.py:1836: RuntimeWarning: invalid value encountered in true_divide\n",
       "  other_rel_err = data['other']['std. dev.'] / data['other']['mean']\n",
-      "/home/nelsonag/git/openmc/openmc/tallies.py:1837: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/home/romano/openmc/openmc/tallies.py:1837: RuntimeWarning: invalid value encountered in true_divide\n",
       "  new_tally._mean = data['self']['mean'] / data['other']['mean']\n"
      ]
     }
@@ -1084,9 +1084,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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JbT24jJc1xwtVNUNV89yfC1W1VTwK11g92+Vz5WG7M2HaMi4cP51tpdZ0ZeIjFT7Mv1wc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\n/BrIB/4P2Bc4F1uPwxhjmi0vvaqmuz9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VAYB+7bdLe967+iAO7t+Bv70zh4uemcb6bVZ8T5TgD/fGNI6HekXkNo7A64JG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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f23842eb048>"
+       "<matplotlib.figure.Figure at 0x7fb30c297748>"
       ]
      },
      "metadata": {},
@@ -1094,9 +1094,9 @@
     },
     {
      "data": {
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C7t6pgj7dOtCve0f6duvI8L5dGda7S9JxC1KwRPOT3zySu59/n7uf/4Cn5i5r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\ncK+kWP3PauCi/IWUOUmnAKc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0rQxKyD06l9G3eyX9e3SiX/dOjOjXjeF9urY6bkEKlmh+4v8O464XP+CuFz/k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\nNBYYO2LEiFyH4pxrRXuXNDw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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f2383a7b0b8>"
+       "<matplotlib.figure.Figure at 0x7fb30c2b2da0>"
       ]
      },
      "metadata": {},
@@ -1104,9 +1104,9 @@
     },
     {
      "data": {
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Xzd1nuvvk4uLiZIciIiluxMAu3H3haJ6+6kgOGdyVm5/7kBNunc1Tb6+muT+o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\nRWAiIpKaGm0cd/dqd/+Hu18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Ti4uLkx2KiKS4Q/t14p6LRvLMNWMYPqAztzw/nxNvm87T76+iqS/Ujbnj1UUc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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f2383a24ef0>"
+       "<matplotlib.figure.Figure at 0x7fb308756710>"
       ]
      },
      "metadata": {},
@@ -1187,9 +1187,9 @@
       "         Copyright | 2011-2017 Massachusetts Institute of Technology\n",
       "           License | http://openmc.readthedocs.io/en/latest/license.html\n",
       "           Version | 0.8.0\n",
-      "          Git SHA1 | 6e3f6bf8b11cb3f6f171f1c351ddcaf85dd515ec\n",
-      "         Date/Time | 2017-02-11 14:12:19\n",
-      "    OpenMP Threads | 8\n",
+      "          Git SHA1 | 7ca46e809ce01fe7857ae36072822a1718f01aaf\n",
+      "         Date/Time | 2017-02-23 09:39:37\n",
+      "    OpenMP Threads | 4\n",
       "\n",
       " ===========================================================================\n",
       " ========================>     INITIALIZATION     <=========================\n",
@@ -1213,56 +1213,56 @@
       "\n",
       "  Bat./Gen.      k            Average k         \n",
       "  =========   ========   ====================   \n",
-      "        1/1    0.98551                       \n",
-      "        2/1    1.05279                       \n",
-      "        3/1    1.03429                       \n",
-      "        4/1    1.01472                       \n",
-      "        5/1    1.01149                       \n",
-      "        6/1    1.07167                       \n",
-      "        7/1    1.00534                       \n",
-      "        8/1    1.02138                       \n",
-      "        9/1    1.03005                       \n",
-      "       10/1    0.99297                       \n",
-      "       11/1    1.00323                       \n",
-      "       12/1    1.00757    1.00540 +/- 0.00217\n",
-      "       13/1    1.02578    1.01219 +/- 0.00691\n",
-      "       14/1    1.01920    1.01395 +/- 0.00519\n",
-      "       15/1    1.02700    1.01656 +/- 0.00479\n",
-      "       16/1    1.02370    1.01775 +/- 0.00409\n",
-      "       17/1    1.00321    1.01567 +/- 0.00403\n",
-      "       18/1    1.01318    1.01536 +/- 0.00351\n",
-      "       19/1    1.02439    1.01636 +/- 0.00325\n",
-      "       20/1    1.03004    1.01773 +/- 0.00321\n",
-      "       21/1    1.01840    1.01779 +/- 0.00291\n",
-      "       22/1    1.00581    1.01679 +/- 0.00284\n",
-      "       23/1    1.01457    1.01662 +/- 0.00261\n",
-      "       24/1    1.03033    1.01760 +/- 0.00261\n",
-      "       25/1    1.00450    1.01673 +/- 0.00258\n",
-      "       26/1    1.03680    1.01798 +/- 0.00272\n",
-      "       27/1    1.02786    1.01856 +/- 0.00262\n",
-      "       28/1    1.01150    1.01817 +/- 0.00250\n",
-      "       29/1    0.99850    1.01714 +/- 0.00258\n",
-      "       30/1    1.02729    1.01764 +/- 0.00250\n",
-      "       31/1    1.02587    1.01803 +/- 0.00241\n",
-      "       32/1    1.01423    1.01786 +/- 0.00231\n",
-      "       33/1    1.05449    1.01945 +/- 0.00272\n",
-      "       34/1    0.99908    1.01861 +/- 0.00274\n",
-      "       35/1    1.02441    1.01884 +/- 0.00264\n",
-      "       36/1    1.01844    1.01882 +/- 0.00253\n",
-      "       37/1    1.03198    1.01931 +/- 0.00249\n",
-      "       38/1    1.05078    1.02043 +/- 0.00265\n",
-      "       39/1    1.01134    1.02012 +/- 0.00257\n",
-      "       40/1    1.01423    1.01992 +/- 0.00249\n",
-      "       41/1    1.03987    1.02057 +/- 0.00250\n",
-      "       42/1    1.05441    1.02162 +/- 0.00264\n",
-      "       43/1    1.02068    1.02160 +/- 0.00256\n",
-      "       44/1    1.06387    1.02284 +/- 0.00277\n",
-      "       45/1    1.04844    1.02357 +/- 0.00279\n",
-      "       46/1    1.01967    1.02346 +/- 0.00272\n",
-      "       47/1    1.01830    1.02332 +/- 0.00264\n",
-      "       48/1    1.00888    1.02294 +/- 0.00260\n",
-      "       49/1    1.03033    1.02313 +/- 0.00254\n",
-      "       50/1    1.02732    1.02324 +/- 0.00248\n",
+      "        1/1    1.00368                       \n",
+      "        2/1    0.99772                       \n",
+      "        3/1    1.04317                       \n",
+      "        4/1    1.02487                       \n",
+      "        5/1    1.01766                       \n",
+      "        6/1    1.03964                       \n",
+      "        7/1    1.02340                       \n",
+      "        8/1    1.02634                       \n",
+      "        9/1    1.03388                       \n",
+      "       10/1    1.02952                       \n",
+      "       11/1    1.01872                       \n",
+      "       12/1    1.02315    1.02093 +/- 0.00222\n",
+      "       13/1    1.02926    1.02371 +/- 0.00306\n",
+      "       14/1    1.01288    1.02100 +/- 0.00346\n",
+      "       15/1    1.04072    1.02494 +/- 0.00477\n",
+      "       16/1    1.01872    1.02391 +/- 0.00403\n",
+      "       17/1    1.00295    1.02091 +/- 0.00454\n",
+      "       18/1    1.02724    1.02170 +/- 0.00401\n",
+      "       19/1    1.01843    1.02134 +/- 0.00355\n",
+      "       20/1    1.02340    1.02155 +/- 0.00318\n",
+      "       21/1    1.03938    1.02317 +/- 0.00331\n",
+      "       22/1    1.00397    1.02157 +/- 0.00342\n",
+      "       23/1    1.03516    1.02261 +/- 0.00331\n",
+      "       24/1    1.03595    1.02357 +/- 0.00321\n",
+      "       25/1    1.03557    1.02437 +/- 0.00309\n",
+      "       26/1    1.03415    1.02498 +/- 0.00296\n",
+      "       27/1    1.02624    1.02505 +/- 0.00278\n",
+      "       28/1    1.02469    1.02503 +/- 0.00262\n",
+      "       29/1    1.00769    1.02412 +/- 0.00264\n",
+      "       30/1    1.02584    1.02420 +/- 0.00251\n",
+      "       31/1    0.99119    1.02263 +/- 0.00286\n",
+      "       32/1    1.00246    1.02172 +/- 0.00287\n",
+      "       33/1    1.00718    1.02108 +/- 0.00282\n",
+      "       34/1    1.01662    1.02090 +/- 0.00270\n",
+      "       35/1    1.01834    1.02080 +/- 0.00260\n",
+      "       36/1    1.04397    1.02169 +/- 0.00265\n",
+      "       37/1    1.01419    1.02141 +/- 0.00256\n",
+      "       38/1    1.02268    1.02145 +/- 0.00247\n",
+      "       39/1    1.04256    1.02218 +/- 0.00249\n",
+      "       40/1    1.03746    1.02269 +/- 0.00246\n",
+      "       41/1    1.02199    1.02267 +/- 0.00238\n",
+      "       42/1    1.03030    1.02291 +/- 0.00232\n",
+      "       43/1    1.00489    1.02236 +/- 0.00231\n",
+      "       44/1    1.03025    1.02259 +/- 0.00225\n",
+      "       45/1    1.05954    1.02365 +/- 0.00243\n",
+      "       46/1    1.03444    1.02395 +/- 0.00238\n",
+      "       47/1    1.00532    1.02345 +/- 0.00237\n",
+      "       48/1    1.03364    1.02371 +/- 0.00232\n",
+      "       49/1    1.02660    1.02379 +/- 0.00226\n",
+      "       50/1    1.04569    1.02434 +/- 0.00227\n",
       " Creating state point statepoint.50.h5...\n",
       "\n",
       " ===========================================================================\n",
@@ -1272,27 +1272,27 @@
       "\n",
       " =======================>     TIMING STATISTICS     <=======================\n",
       "\n",
-      " Total time for initialization     =  3.6247E-02 seconds\n",
-      "   Reading cross sections          =  2.9810E-03 seconds\n",
-      " Total time in simulation          =  7.6874E+00 seconds\n",
-      "   Time in transport only          =  7.5290E+00 seconds\n",
-      "   Time in inactive batches        =  5.9831E-01 seconds\n",
-      "   Time in active batches          =  7.0890E+00 seconds\n",
-      "   Time synchronizing fission bank =  5.0012E-03 seconds\n",
-      "     Sampling source sites         =  3.4413E-03 seconds\n",
-      "     SEND/RECV source sites        =  1.4835E-03 seconds\n",
-      "   Time accumulating tallies       =  1.1537E-04 seconds\n",
-      " Total time for finalization       =  3.0480E-06 seconds\n",
-      " Total time elapsed                =  7.7461E+00 seconds\n",
-      " Calculation Rate (inactive)       =  83568.1 neutrons/second\n",
-      " Calculation Rate (active)         =  28212.6 neutrons/second\n",
+      " Total time for initialization     =  2.6345E-02 seconds\n",
+      "   Reading cross sections          =  3.9616E-03 seconds\n",
+      " Total time in simulation          =  1.6005E+01 seconds\n",
+      "   Time in transport only          =  1.5841E+01 seconds\n",
+      "   Time in inactive batches        =  1.3158E+00 seconds\n",
+      "   Time in active batches          =  1.4689E+01 seconds\n",
+      "   Time synchronizing fission bank =  6.9577E-03 seconds\n",
+      "     Sampling source sites         =  4.4024E-03 seconds\n",
+      "     SEND/RECV source sites        =  2.4544E-03 seconds\n",
+      "   Time accumulating tallies       =  1.4749E-04 seconds\n",
+      " Total time for finalization       =  3.7060E-06 seconds\n",
+      " Total time elapsed                =  1.6053E+01 seconds\n",
+      " Calculation Rate (inactive)       =  37998.4 neutrons/second\n",
+      " Calculation Rate (active)         =  13615.9 neutrons/second\n",
       "\n",
       " ============================>     RESULTS     <============================\n",
       "\n",
-      " k-effective (Collision)     =  1.02169 +/-  0.00234\n",
-      " k-effective (Track-length)  =  1.02324 +/-  0.00248\n",
-      " k-effective (Absorption)    =  1.02565 +/-  0.00155\n",
-      " Combined k-effective        =  1.02525 +/-  0.00163\n",
+      " k-effective (Collision)     =  1.02412 +/-  0.00199\n",
+      " k-effective (Track-length)  =  1.02434 +/-  0.00227\n",
+      " k-effective (Absorption)    =  1.02703 +/-  0.00143\n",
+      " Combined k-effective        =  1.02632 +/-  0.00142\n",
       " Leakage Fraction            =  0.00000 +/-  0.00000\n",
       "\n"
      ]
@@ -1373,9 +1373,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Continuous-Energy keff = 1.024739\n",
-      "Multi-Group keff = 1.025255\n",
-      "bias [pcm]: -51.5\n"
+      "Continuous-Energy keff = 1.030194\n",
+      "Multi-Group keff = 1.026316\n",
+      "bias [pcm]: 387.9\n"
      ]
     }
    ],
@@ -1472,7 +1472,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x7f238421fc18>"
+       "<matplotlib.text.Text at 0x7fb30c1c73c8>"
       ]
      },
      "execution_count": 41,
@@ -1481,9 +1481,9 @@
     },
     {
      "data": {
-      "image/png": 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NLf84ITboqljCxAy+NjXDoWyfoa7+cV2Ps3uoGXrzU3FdozLs160Z9itORQCHxZIu28WD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\nQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f2383cc7898>"
+       "<matplotlib.figure.Figure at 0x7fb30877fa20>"
       ]
      },
      "metadata": {},
@@ -1518,8 +1518,9 @@
   }
  ],
  "metadata": {
+  "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python [default]",
    "language": "python",
    "name": "python3"
   },
@@ -1533,7 +1534,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.0"
+   "version": "3.5.2"
   }
  },
  "nbformat": 4,
diff --git a/openmc/geometry.py b/openmc/geometry.py
index 5f651af2f..a272eec0a 100644
--- a/openmc/geometry.py
+++ b/openmc/geometry.py
@@ -208,13 +208,16 @@ class Geometry(object):
         return material_cells
 
     def get_all_material_universes(self):
-        """Return all universes having at least one non-fill cell
+        """Return all universes having at least one material-filled cell.
+
+        This method can be used to find universes that have at least one cell
+        that is filled with a material or is void.
 
         Returns
         -------
         collections.OrderedDict
             Dictionary mapping universe IDs to :class:`openmc.Universe`
-            instances with non-fill cells
+            instances with at least one material-filled cell
 
         """
         material_universes = OrderedDict()
diff --git a/openmc/mgxs/library.py b/openmc/mgxs/library.py
index 6b0bc3913..13fbe5e87 100644
--- a/openmc/mgxs/library.py
+++ b/openmc/mgxs/library.py
@@ -3,7 +3,7 @@ import os
 import copy
 import pickle
 from numbers import Integral
-from collections import OrderedDict
+from collections import OrderedDict, Iterable
 from warnings import warn
 
 from six import string_types
@@ -315,16 +315,16 @@ class Library(object):
         # User specified a list of material, cell or universe domains
         else:
             if self.domain_type == 'material':
-                cv.check_iterable_type('domain', domains, openmc.Material)
+                cv.check_type('domain', domains, Iterable, openmc.Material)
                 all_domains = self.geometry.get_all_materials().values()
             elif self.domain_type in ['cell', 'distribcell']:
-                cv.check_iterable_type('domain', domains, openmc.Cell)
+                cv.check_type('domain', domains, Iterable, openmc.Cell)
                 all_domains = self.geometry.get_all_material_cells().values()
             elif self.domain_type == 'universe':
-                cv.check_iterable_type('domain', domains, openmc.Universe)
+                cv.check_type('domain', domains, Iterable, openmc.Universe)
                 all_domains = self.geometry.get_all_universes().values()
             elif self.domain_type == 'mesh':
-                cv.check_iterable_type('domain', domains, openmc.Mesh)
+                cv.check_type('domain', domains, Iterable, openmc.Mesh)
 
                 # The mesh and geometry are independent, so set all_domains
                 # to the input domains
@@ -339,7 +339,7 @@ class Library(object):
                     raise ValueError('Domain "{}" could not be found in the '
                                      'geometry.'.format(domain))
 
-            self._domains = domains
+            self._domains = list(domains)
 
     @energy_groups.setter
     def energy_groups(self, energy_groups):