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Expand documentation on MD and opt; add "Foreword and FAQ" (#5337)

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10
docs/getting-started/first-calculation.md
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@ -126,14 +126,16 @@ is spent in grid operations, sparse matrix operations, diagonalization, or commu
## Next Steps
- Converge [CUTOFF](#CP2K_INPUT.FORCE_EVAL.DFT.MGRID.CUTOFF) and
[REL_CUTOFF](#CP2K_INPUT.FORCE_EVAL.DFT.MGRID.REL_CUTOFF): [](../methods/dft/cutoff)
- If this example calculation has been executed on the cloud, consider building or installing CP2K
on your server: [](build-from-source), [](build-with-spack), [](distributions)
- Learn the idea behind GPW: [](../methods/dft/gpw)
- Learn about basis sets and pseudopotentials: [](../methods/dft/basis_sets),
[](../methods/dft/pseudopotentials)
- Build or install CP2K: [](build-from-source), [](build-with-spack), [](distributions)
- Converge [CUTOFF](#CP2K_INPUT.FORCE_EVAL.DFT.MGRID.CUTOFF) and
[REL_CUTOFF](#CP2K_INPUT.FORCE_EVAL.DFT.MGRID.REL_CUTOFF): [](../methods/dft/cutoff)
- Proceed to geometry optimization: [](../methods/optimization/geometry_and_cell_opt)
- Proceed to molecular dynamics simulation: [](../methods/sampling/molecular_dynamics)
- Explore more complete examples: <https://github.com/cp2k/cp2k-examples>
- Read the practical CP2K overview paper: [](#Iannuzzi2026)
```{youtube} qMR-NAaUheg
---

252
docs/getting-started/foreword-and-faq.md Normal file
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@ -0,0 +1,252 @@
# Foreword and FAQ
It is our great pleasure to present CP2K, an open-source software package for _ab initio_ electronic
structure calculations in atomistic simulations. The code is written in Fortran 2008 and has been
geared towards large-scale, high-performance CPU and GPU computation with multi-threading, MPI, CUDA
and HIP parallelization. For an overview of the capabilities, see
[Features](https://www.cp2k.org/features).
While CP2K started as an implementation of quantum chemical methods (more specifically, the
`QUICKSTEP` module, as presented at [](#VandeVondele2005)) for molecular dynamics simulation,
decades of ensuing development has witnessed a vast team of collaborators with their innumerable
contributions and the ever-growing user base with their valuable feedbacks, to whom we wish to
express sincere gratitude. As CP2K is freely available in various ways and does not requite
registration to use, it is difficult to gather accurate usage stats; but the
[list of publications using CP2K](https://www.cp2k.org/science) speaks for itself.
We would like to ask you, users of CP2K, to acknowledge our work by citing the publications as
listed on the [Bibliography](../bibliography) page and printed as REFERENCES at the end of output
log of the program, in particular the review articles:
- [](#K%C3%BChne2020), on the theoretical background and algorithms;
- [](#Iannuzzi2026), on the practical usage and applications.
We have prepared a list of Q&A for frequently asked things below, which we hope can be helpful for
the experience with the CP2K package and the art of computational chemistry in general.
This program is provided "as-is" without any expressed or implied warranty.
## Firstly, what does the name CP2K stand for?
Simply put, "CP" means Car-Parrinello, the initials of two scientists, and "2K" means year 2000.
Historically there were two formulations developed for _ab initio_ molecular dynamics ({term}`MD`):
the Car-Parrinello Molecular Dynamics ({term}`CPMD`), and the Born-Oppenheimer Molecular Dynamics
({term}`BOMD`). A program named simply also as `CPMD` began its development back in the 1990s
featuring the Car-Parrinello Molecular Dynamics; the sister project, named as CP2K, would become its
spiritual successor in the wake of the New Millenium.
Due to the fact that the original CPMD formulation has not actually been implemented yet, the name
CP2K may sound slightly non-indicative. The BOMD formulation is the major one employed by CP2K and
has seen mainstream applications in a variety of fields in the 21st century.
## Can I try CP2K out somewhere before installation?
Yes. The [CP2K Lab](https://lab.cp2k.com/) is a spin-off commercial platform set up by developers
for building structures, writing input files, executing jobs on the cloud and analyzing the outputs.
Once signed up for free, the free-tier features already allow for experiments with lightweight
computation like the one in [](./first-calculation). For those in need of more resources and
functionalities, the platform offers paid tier, site license and on-premise enterprise support.
## What preliminary knowledge does using CP2K need?
Practically CP2K is built and executed in some Linux-based operating systems, ranging from on
physical high-performance computers for production to in virtual machines for quick small tests.
This implies the need of Linux knowledge including its file system, paths, user privileges and
permissions, environment variables, shells (most commonly Bash and POSIX), utility commands,
stdin/stdout/stderr, piping and redirects, shell scripts, and modules and library files. In
addition, having some experience with Fortran, C, and C++ compilers as well as CMake will be helpful
for configuration and installation. Optionally, learn about upper-level management via job
schedulers and queue systems.
On the science side, introductory courses on chemistry, solid-state physics, and statistical
mechanics are vital prerequisites before carrying out computer simulations just as before performing
experiments in real life. Moreover, it is mandatory to have a clear understanding about the
theoretical methods in simulation; their characteristics and performance, strengths and limitations
should be described in the publications in the original conception and follow-up benchmarks. There
are two polar opposite pitfalls to be avoided: it is easy to overlook the subtleties and adjust
input settings mindlessly hoping that the black box somehow works, but it is also easy to become
absorbed in the maths and spend a lot of time trying to work out the equations that is not the focus
of the actual research project.
```{note}
Worth stressing are two overarching aspects of computer simulation:
- In spite of ever-growing scientific computing power, most of the time it is not affordable to have
an exact 1:1 computational model of the real-life phenomena of interest. The usual practice is to
use a much scaled-down model with limited number of atoms and finite length of trajectory for the
simulation, which should achieve the delicate balance between representativeness and feasibility.
The discrepancy of space and time scales between simulation and reality can be easily neglected due
to a lack of awareness of the kinetics, especially for slow, rare events with high energy barrier
that can only be observed in an extended period of time in real life.
- There is no need to worry if a theoretical method is strictly *ab initio* or not; both styles of
deriving methods, "starting from physically rigorous and universal first principles" and "taking
empirical results into account by fitting parameters with extra data", are capable of producing
useful algorithms and accurate results depending on the case. The real concern is better put on
the performance of methods on the target system of interest, which should have been benchmarked
in existing works of the particular subdivision of science; also, the similarity between primitive
datasets on which empirical parameters of the method (if any) are fitted and the target system
of interest can be telling.
```
## How do I create the atomistic model for CP2K input?
This is done with external visualization and construction programs. Considering that a task of
geometry and/or cell optimization is usually the very first CP2K job, some general rules are
discussed on the
[relevant documentation page](../methods/optimization/geometry_and_cell_opt.md#starting-structure-and-cell).
If available, *computational* databases and benchmark sets are the most recommended avenue to obtain
structures due to having already been subject to some electronic-structure calculation. Even the
cheap methods and loose thresholds in a high-throughput screening and optimization can make the
structure qualitatively reasonable by chemical and physical intuitions, although further
optimization is still needed.
On the other hand, structures that are from *experimental* characterization are frequently not
"computation-ready", and thus should not be subject to computation without careful validation in
pre-processing. This can be prominent for `cif` and `pdb` structures determined by powder or single-
crystal XRD which can be affected by sample quality and thermal motion.
- Watch out for crystallographic disorder and atoms with low resolution or fractional occupation:
using the superposition of all atoms as if every occupancy is 1.00 is highly likely to introduce
contacting or even overlapping atoms.
- Beware of composition: the atomic structure may not match the intended macroscopic, charge-neutral
chemical formula, owing to missing or duplicated hydrogen atoms, small counter ions, solvent or
ligand molecules.
Possible resolutions vary from simple manual editing in the modelling stage, to utilization of
supercells and enumeration of special quasirandom structures (common for materials with dopants),
and to more rigorous XRD refinement and application of quantum crystallography methods. It is
believed that further advancements in instrumental analysis and structure resolution techniques
would eventually benefit computational chemistry greatly.
## Do I need PBC for my model?
**Periodic boundary condition** ({term}`PBC`) is a fundamental feature of CP2K, covering the full
range of dimensionalities of translational symmetry from 3D, 2D, 1D to 0D. The key distinction is
how connectivity, neighbor lists and integration grids are generated, how the Poisson solver handles
the electrostatic interaction, and how translational and rotational degrees of freedom of the center
of mass (i.e. collective motion as a whole) are treated.
If the structure involves condensed-phase matter, such as liquid solution, solid crystal, surface
slab and other one- and two-dimensional nano-materials, then generally PBC is used. This is also
applicable to systems with no actual well-defined repeating units like the bulk solutions. A huge
liquid droplet in the gaseous phase, where the diameter is so large that the gas-liquid interface is
almost flat and surface tension is negligible, may just as well be modelled as a combination of a
bulk solution system and an interface between a gaseous/vacuum region and a thin layer of solution,
both of which make use of PBC even though the liquid droplet itself is not periodic. However, it may
be necessary to validate the size of PBC against target properties to confirm that it is
sufficiently large for sampling, sometimes with the minimum image convention in mind.
Isolated molecular clusters in the gaseous phase or vacuum, where external pressure is irrelevant,
can be simulated without PBC. A frequent question is why a molecule optimized in vacuum does not
match its crystal structure; this is because the ordered packing pattern in the crystalline form
creates an environment capable of driving conformational changes. Oftentimes literatures convert a
periodic structure to an isolated model of finite size and apply modifications on the edge in the
form of terminal capping atoms/groups or point charges; these treatments are usually intended to
adapt the structure to quantum chemical softwares with no PBC support, but in CP2K they may not
offer extra advantages over an appropriate PBC for translational symmetry.
In certain cases, the same process can be simulated both with and without PBC. For example, the
reaction between hydroxyl and hydrogen may be modelled as a single $\mathrm{H_2}$ molecule colliding
with a single $\mathrm{OH}$ molecule with different relative orientations, distances and velocities,
which does not need PBC, or modelled as a mixture of numerous $\mathrm{H_2}$ and $\mathrm{OH}$
molecules, which needs PBC. Their behavior regarding responses to external conditions including
temperature, pressure, and any form of energy input may be different, but they provide insights from
distinct perspectives.
## Does CP2K support k-points?
As an essential element for solid-state electronic structure, there is of course support for
k-points in a broad sense in the `QUICKSTEP` module of CP2K. A few specialized features may not have
complete, verified program implementations for k-point support, or are based on theories and
algorithms that do not have an updated k-point version (compared with an isolated, non-periodic
formalism) to begin with. After all, it is not a far stretch to think that a novel k-point
generalization to existing methods is worthy of one or more academic publications and takes serious
collaboration and devoted efforts to investigate.
The development status and user opinions about k-point supports can be found at the dedicated
[github issue](https://github.com/cp2k/cp2k/issues/4854); any request for new features of this kind
requires providing a reference implementation of k-point formalism in other softwares.
## Where can I meet the CP2K community?
Several discussion venues are available:
- The [User Forum](https://groups.google.com/group/cp2k) hosted on Google Groups, with a read-only
[mirror](https://lists.cp2k.org/listinfo/cp2k-user) and a downloadable
[archives](https://lists.cp2k.org/archives/cp2k-user/). To use the forum, sign in with a Google
account, apply to join and then wait for approval.
- The [issues](https://github.com/cp2k/cp2k/issues/) and
[discussions](https://github.com/cp2k/cp2k/discussions) of the official github repository.
- The
[Matter Modeling Stack Exchange](https://mattermodeling.stackexchange.com/questions/tagged/cp2k)
has, among other topics, a tag for CP2K.
- For Chinese users, there is also a CP2K category in the First-principles subforum of the
[Computational Chemistry Commune](http://bbs.keinsci.com/forum-105-1.html?typeid=42).
Please note that the github issues and discussions are only intended for topics relevant to the
program development and code implementation, such as reproducible bug reports, well-defined feature
requests and revisions to the documentation or manual. For more general help on the usage, as well
as unexpected behaviors that may or may not be bugs, check the other venues first; experts can
handle the questions and determine if they are eligible to be brought to github issues.
## What is the best practice to ask questions?
The general etiquette for requesting tech support online has been summarized nicely by Eric S.
Raymond's [How To Ask Questions The Smart Way](http://www.catb.org/~esr/faqs/smart-questions.html).
(**Disclaimer**: this link does not imply any connection between the original author and the CP2K
developers, nor does it suggest that the original author may be contacted for assistance.)
Before submitting a question, please compose it with sufficient details, accuracy, and clarity.
Approach the process in the same way as making a presentation to general audience, or even writing
the "Methods" section in a formal academic publication; this includes giving explanations to
uncommon acronyms (say, the abbreviated name of a specific class of materials, or anything that is
not on the [Acronyms](../acronyms) page) and traceable citations (with publication title, date, and
DOI, instead of merely showing a screenshot or a paragraph of copy-pasted text). The release date or
git version of CP2K, and custom revisions if any, has to be mentioned in the first place.
For problems related to installation and/or performance, the hardware specification and the
configuration for linked libraries should be explained. The distribution source and means of
preparation of dependencies, like with package managers, environment-controlling modules, or just a
build from source, need clarifying.
For error terminations and wrong results, it is imperative to provide a complete input deck and the
output files. The "input deck" encompasses not only the main input file with keyword settings, but
also all of the external files referenced inside unless they are available under the official `data`
directory, so that the job can be actually run and tested. Instead of the original intended chemical
structure and composition, it is better to use a simplified system that triggers the malfunction
reliably; this prevents confidential research information to be disclosed and reduces the demand on
computational resources to ease the load of computers on the developer side.
Please refrain from talking about CP2K-specific suggestions from generic large language model (LLM)
or other types of artificial intelligence (AI). Even if the AIs have been trained on a refined and
verified corpus of CP2K-oriented information one day, they can still hallucinate and generate
superficially convincing but scientifically incorrect responses. As with academic publications, the
human author is responsible for the correctness of any content produced by AIs.
Lastly, please kindly understand that, despite the CP2K developers having knowledge about the
algorithm infrastructures and program implementations, they may not be suitable for answering all of
the questions arising from practice, especially those pertaining to niche research areas where
apprehending the science and acquiring the skills will require much more extensive academic training
than learning to use a program. The best party to consult for guidance of this type would be the
tutor, advisor, experienced colleagues or collaborators in real life, and when attempting to
reproduce reported findings, the original authors. This is not denying any personal potential to
teach oneself at no cost, but rather hinting the necessity of communicating with the right
professional people which does not have substitutes.
## May I join in development and send patches?
Certainly! CP2K welcomes all sorts of contributions, from a small typo fix to modular code
refactoring, to interfaces with other packages, to novel implementation of cutting-edge
technology... Sharing kindness is an easy feat, and patches makes it more complete, that is the
essence of open-source programming.
The CP2K project uses `git` as the version control tool, and the official code repository is on
github as [cp2k](https://github.com/cp2k/cp2k). For detailed instructions see the page
[Starting development](https://www.cp2k.org/dev:starting).
Another form of contribution is to enrich the [cp2k-examples](https://github.com/cp2k/cp2k-examples)
repository with example inputs and outputs, complete with post-analysis workflow down to straight
publishable results and discussions if possible. This will help other curious users see the full
potential of CP2K in terms of scientific and engineering applications.

1
docs/index.md
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@ -16,6 +16,7 @@ caption: Getting Started
titlesonly:
maxdepth: 1
---
getting-started/foreword-and-faq
getting-started/build-from-source
getting-started/build-with-spack
getting-started/distributions

43
docs/methods/optimization/geometry_and_cell_opt.md
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@ -37,7 +37,7 @@ relaxed in-plane lattice constants, and structures prepared for fixed-cell molec
```{note}
An optimization is not a replacement for finite-temperature pressure sampling; if the desired
quantity is a thermal average at finite temperature, use a molecular-dynamics workflow with
appropriate ensemble instead.
appropriate ensemble instead. For more on this topic, see [](../sampling/molecular_dynamics).
For the usual electronic-structure methods based on the Born-Oppenheimer approximation
(which, by neglecting nuclear motion, provides the concept of "potential-energy surface"
@ -146,7 +146,12 @@ vaporized or molten conditions with lots of broken chemical bonds.
For `CELL_OPT`, the initial cell matters as much as the initial coordinates. The starting volume and
shape should be close enough to the expected structure and density, such that the pressure and
stress are not dominated by preparation artefacts.
stress are not dominated by preparation artefacts. The directions where the cell is relaxed are
dependent of the external pressure. For surface slabs, two- or one-dimensional materials, or systems
with vacuum, the anisotropic nature means that the vacuum direction is not to be relaxed unless
physically intended; it is better to constrain the appropriate cell components or use a fixed-cell
optimization after choosing the desired cell. A rigorous test for the convergence of target
properties with respect to different size of vacuum may be necessary.
A visualization of the structure and cell in modelling programs, with the box and the neighboring
periodic images displayed, will be very helpful; neither large vacuous gaps nor crowded cluster of
@ -154,33 +159,11 @@ atoms should occur near the boundary of the box on the directions consistent wit
and conversely, sufficient vacuum space on the non-periodic directions is crucial for eliminating
unwanted interactions across the boundary.
For surface slabs, two- or one-dimensional materials, or systems with vacuum, do not relax the
vacuum direction unless physically intended; it is better to constrain the appropriate cell
components or use a fixed-cell optimization after choosing the desired cell.
```{warning}
**Do not use experimental structure blindly.**
If available, **computational** materials databases are the most recommended avenue
to obtain structures that are "computation-ready", or even better, already optimized
with some electronic-structure methods. On the other hand, structures that are from
**experimental** characterization are frequently not "computation-ready", and thus
should not be subject to optimization without careful validation in pre-processing.
This can be prominent for `cif` and `pdb` structures determined by powder or single-
crystal XRD which can be affected by sample quality and thermal motion.
- Watch out for crystallographic disorder and atoms with low resolution or fractional
occupation: using the superposition of all atoms as if every occupancy is 1.00 is
highly likely to introduce contacting or even overlapping atoms.
- Beware of composition: the atomic structure may not match the intended macroscopic,
charge-neutral chemical formula, owing to missing or duplicated hydrogen atoms,
small counter ions, solvent or ligand molecules.
Possible resolutions vary from simple manual editing in the modelling stage, to
utilization of supercells and enumeration of special quasirandom structures (common
for materials with dopants), and to more rigorous XRD refinement and application of
quantum crystallography methods.
```
As a reserved extreme measure, the [MAX_FORCE](#CP2K_INPUT.FORCE_EVAL.RESCALE_FORCES.MAX_FORCE)
keyword triggers a mechanism where very large forces on atoms are artificially rescaled in
magnitude. This setting is only meant for crude initial structure; once the geometry becomes more
reasonable and the forces are closer to the convergence criteria, it shall not be used in the
production run towards the final result.
```{note}
For variable-cell optimizations, the [CELL_OPT](#CP2K_INPUT.MOTION.CELL_OPT) section
@ -407,7 +390,7 @@ and linked to the CP2K build in order to detect and preserve the space group. Us
Density functional theory (DFT) is an electronic-structure (wavefunction) method routinely used for
optimization. This section elaborate on the relevant aspects, assuming basic knowledge about the
method which can be found at [](../dft/index.md).
method which can be found at [](../dft/index).
### SCF quality

110
docs/methods/sampling/molecular_dynamics.md
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@ -46,12 +46,98 @@ A minimal fixed-energy MD block is:
The force method is defined independently in [FORCE_EVAL](#CP2K_INPUT.FORCE_EVAL).
## Initial structure and velocities
## Preliminary considerations
Start from a physically reasonable structure with sensible bond lengths, intermolecular distances,
density, and cell parameters. MD is not a reliable way to fix severe close contacts, unrealistic
densities, or badly prepared cells, because such problems can cause unstable forces, SCF failures,
or immediate heating and atom ejection. A geometry optimisation before MD can be helpful.
Universal to every atomistic simulation and every program, a number of crucial points must be
carefully considered before setting up a molecular dynamics task and investing copious amounts of
computational resources. As a starter, this section discusses preliminary factors in the choice of
the spatiotemporal scale of the trajectory, periodic boundary condition. force method, and the
initial structure.
### Space and time scale
Even when taking the ergodic hypothesis as granted, statistical analysis on target properties at
thermodynamic equilibrium has to be performed on top of sufficient sampling. The (auto)correlation
length and (auto)correlation time provide estimates for the space and time scale where measurements
of some property retain a memory of past states and are not fully statistically independent. It is
important to learn about their typical values in the same or similar systems, and take adequate
multiples as the size of the system and the length of the trajectory.
On the other hand, non-equilibrium irreversible processes such as phase transitions and chemical
reactions are also observable at some time and space resolution. Experimentally it is determined by
the sensitivity of instrumental characterization and analysis methods, with certain precise
definitions of the states before and after the process. Unfortunately, many experiments take place
in real life in a macroscopic time interval that exceeds the capability of simulation; for instance,
a chemical reaction taking days, hours, or even just seconds to happen may not be replicatable in
AIMD simulations with trajectory on the picosecond or nanosecond scale starting with only a bunch of
reactants randomly packed together, however thermodynamically favorable it is. Again, it is better
to survey literatures with relevant kinetic data and have a clear understanding of the typical free
energy barriers and reaction rates before proceeding to simulations. Even static thermochemistry
calculations for the profile of energy landscape via optimization of minima and transition states,
vibrational analysis and evaluation of single-point energy would be helpful. To simulate difficult
reactions by molecular dynamics in a short trajectory, advanced _enhanced sampling_ methods such as
[](./metadynamics) may be necessary.
### Periodic boundary condition
**Periodic boundary condition** ({term}`PBC`) may or may not be used in the molecular dynamics
simulation, and there are caveats for both ends.
On one hand, very small periodic cells for production MD should be avoided unless the finite-size
effects are known to be acceptable. Small cells produce artificial correlations through periodic
images, exaggerate statistical temperature and pressure fluctuations, and can make NPT cell dynamics
unstable or unphysical. Sometimes using a supercell of a crystalline structure is preferred if its
primitive cell is too small.
On the other hand, without PBC in MD, a common risk is that strong repulsion or very weak attraction
causes the system to expand and effectively diminishes desired interactions between molecules. A
possible remedy is to define an external potential or a constraint that act as a soft wall to
reflect or push stray molecules back to the center, but this is artificial and arbitrary.
### Force method
To propagate the trajectory in a molecular dynamics simulation, the energy and force is evaluated on
each timestep to provide acceleration via Newton's second law. The choice of the force method
determines the description of chemical bonding and/or weak interactions, the configuration space
that can be sampled, and the eventual computational cost. Careful selection of function forms and
parameters as well as thorough validation against the target property is vital to any application;
in particular, the part of "cross" interactions between two or more components described with more
than one unified force method requires very well-founded justification.
The strength and limitation of each force method is decided by design and parametrization. For
instance, classical force fields in molecular mechanics usually model chemical bonds by harmonic
potentials on the stretching, bending and rigid dihedral terms (and periodic torsion potentials on
the flexible dihedral terms). The simplicity of function forms allows for speedy evaluation and the
conformational fluctuation or conversion can be captured well, but the bonding pattern is fixed to
near equilibrium and bond breaking and forming in chemical reactions cannot be captured. To describe
the bond rearrangement and the underlying electronic structure, AIMD based on quantum chemical
methods becomes requisite even though it is slower and more costly. The middle grounds have recently
becoming filled with emerging methods like the machine-learning potentials.
### Initial structure
Because most of the points apply here as well, please first refer to the documentation about the
[starting structure and cell](../optimization/geometry_and_cell_opt.md#starting-structure-and-cell)
of a geometry optimization.
In general, start from a physically reasonable structure with sensible bond lengths, intermolecular
distances, density, and cell parameters. A majority of the force methods demonstrate extremely
strong repulsion at very small nuclei distances, and thus molecular dynamics simulation employing
these methods is not a reliable way to fix severe close contacts, unrealistic densities, or badly
prepared cells, because such problems can cause unstable forces, SCF failures, or immediate heating
and atom ejection.
In fact, when starting from scratch, a geometry optimization preceding molecular dynamics simulation
is highly recommended. During geometry optimization, the structure is relaxed and the maximum force
on atoms is significantly lowered, which puts an upper limit to the initial acceleration in the
subsequent molecular dynamics simulation and prevents rapid atomic motion and extreme temperature
rise. With electronic-structure methods, the geometry optimization also offers a chance to confirm
SCF convergence and estimate the time consumption on each step. The only "downside" is that the
temperature may start from or drop to a relatively low value at early simulation due to the small
forces and slow motion, and may take a little longer to heat up to the desired temperature; see the
section on equilibration below for how to address this.
## Initial velocities
If velocities are not provided by a restart file or an external trajectory, CP2K can initialise
atomic velocities from [TEMPERATURE](#CP2K_INPUT.MOTION.MD.TEMPERATURE). The
@ -65,11 +151,6 @@ meaningful, overall angular motion; see [COMVEL_TOL](#CP2K_INPUT.MOTION.MD.COMVE
and related keywords. For periodic bulk systems, small total-momentum drift is usually less
important.
**Avoid very small periodic cells for production MD unless the finite-size effects are known to be
acceptable.** Small cells produce artificial correlations through periodic images, exaggerate
statistical temperature and pressure fluctuations, and can make NPT cell dynamics unstable or
unphysical.
## Choosing an ensemble
The most common choices are `NVE`, `NVT`, `NPT_I`, `NPT_F`, and `LANGEVIN`.
@ -269,11 +350,12 @@ trajectory can be continued without losing significant sampling time.
## Validation checklist
Before running a long production trajectory, it's suggested to check that:
Before running a long production trajectory, it is suggested to check that:
- The affordable length of trajectory is sufficient for observing phenomena, for instance chemical
reactions need to be kinetically favorable so as to happen on a picosecond timescale that AIMD
usually manages to cover.
- The planned length of trajectory and reserved computational time is long enough to overcome
(auto)correlations and/or observe phenomena;
- The force method and key parameters is validated on the particular system and is suitable for
describing the target property;
- The structure is physically reasonable and the shortest interatomic distances are sensible;
- The periodic cell is large enough for the target property;
- The SCF procedure converges reliably on representative snapshots;