Installation and quick start guide

The pymatnest package is a software library for carrying out nested sampling calculations. It can be used to explore the energy landscape of different materials (both clusters and bulk materials), calculate thermodynamic variables at arbitrary temperatures, locate phase transitions and calculate the phase diagram. It can be used with LAMMPS, and the supplied fortran models, and both with MC and MD.

If you use pymatnest, please cite the following publications: (references are available in bibtex format in the NS_publications.bib file)

L.B. Partay, A.P. Bartok, G. Csanyi, Efficient Sampling of Atomic Configurational Spaces, J. Phys. Chem. B (2010), 114, 10502–10512, https://pubs.acs.org/doi/abs/10.1021/jp1012973

R.J.N. Baldock, L.B. Partay, A.P. Bartok, M.C. Payne, G. Csanyi, Determining pressure-temperature phase diagrams of materials, Phys. Rev. B (2016), 93, 174108, https://journals.aps.org/prb/abstract/10.1103/PhysRevB.93.174108

R.J.N. Baldock, N. Bernstein, K. M. Salerno, L.B. Partay, G. Csanyi, Constant-pressure nested sampling with atomistic dynamics, Phys. Rev. E (2017), 96, 043311, https://link.aps.org/doi/10.1103/PhysRevE.96.043311

L.B. Partay, G. Csanyi and N. Bernstein Nested sampling for materials, Eur. Phys. J. B (2021) 94, 159, https://link.springer.com/article/10.1140/epjb/s10051-021-00172-1

Further examples of publications using the pymatnest package:

L.B. Partay On the performance of interatomic potential models of iron: Comparison of the phase diagrams, Comp. Mat. Sci. (2018), 149, 153, https://www.sciencedirect.com/science/article/pii/S0927025618301794

J. Dorrell, L.B. Partay Thermodynamics and the potential energy landscape: case study of small water clusters, Phys. Chem. Chem. Phys. (2019), 21, 7305 https://pubs.rsc.org/en/content/articlehtml/2019/cp/c9cp00474b

A.P. Bartok, G. Hantal and L.B. Partay Insight into Liquid Polymorphism from the Complex Phase Behavior of a Simple Model, Phys. Rev. Lett. (2021) 127, 015701 https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.127.015701

L.B. Partay and G. Hantal Stability of the high-density Jagla liquid in 2D: sensitivity to parameterisation, Soft Matter, (2022) 18, 5261 https://pubs.rsc.org/en/content/articlelanding/2022/SM/D2SM00491G

M. Yang, L.B. Partay and R.B. Wexler Surface phase diagrams from nested sampling, Phys. Chem. Chem. Phys., (2024),26, 13862, https://pubs.rsc.org/en/content/articlehtml/2024/cp/d4cp00050a

System requirements

You have to have numpy and ASE installed (check that you have a newer ASE version otherwise the extended xyz version will not be recognised). You have to have mpi4py and psutil too.

Compilation of the streamlined FORTRAN models and MC/MD walkers.

  1. edit Makefile.arch (by default Linux appropriate variables are set)

  2. make

Using with LAMMPS

These instructions assume the latest (git/svn) version of LAMMPS. It is not tested how far back older versions would also work.

Mandatory compilation flags (for all LAMMPS versions)

It is necessary to take advantage of the -DLAMMPS_EXCEPTIONS flag, which allows lammps crashes to be handled gracefully within python. Add it to the LMP_INC variable in the LAMMPS makefile before compiling.

Basic instructions for recent versions of LAMMPS and mpi4py version 2.0.0 or newer

Create an appropriate LAMMPS serial makefile, and compile with

  • make [machine] mode=shlib

For example make serial mode=shlib. Install the python files supplied with LAMMPS :

  • make install-python

Copy lammps_top_dir/src/liblammps_[machine].so to the same place where LAMMPS installed the python packages. (Likely to be in the PYTHONAPTH, where a lammps directory is created).

The input file variable LAMMPS_name is what you set for [machine] when installing lammps_[machine].so. By default it is what you set for machine when compiling LAMMPS, unless the library was renamed when installing.

Basic instructions for LAMMPS versions Nov 2020 or before:

Create an appropriate LAMMPS parallel makefile, and compile with

  • make [machine] mode=shlib

Copy lammps_top_dir/python/lammps.py to a directory set in your PYTHONPATH.

Copy lammps_top_dir/src/liblammps_[machine].so to the same place where you copied lammps.py.

The input file variable LAMMPS_name is what you set for [machine] when installing lammps_[machine].so. By default it is what you set for machine when compiling LAMMPS, unless the library was renamed when installing. Set the input variable LAMMPS_serial=F.

Support for GMC within LAMMPS

Copy the two GMC-related files ns_run_dir/lammps_patches/fix_gmc.* to the LAMMPS directory lammps_top_dir/src/ before compiling, and set LAMMPS_fix_gmc=T in the input file.

Support for molecules

For using molecules use the latest version of LAMMPS. If you need, copy the improper related files ns_run_dir/lammps_patches/create_* to the LAMMPS directory lammps_top_dir/src/ before compiling. See the file example_inputs/inputs.test.cluster.MD.lammps.polymer for an example.

Mixed MPI-OpenMP

It is possible to use OpenMP to parallelize each LAMMPS task. This has been tested to run, but not for correctness or efficiency.

  • cd lammps_top_dir/src

  • make yes-user-omp

Add openmp enabling flag (e.g. -fopenmp for gfortran) to CCFLAGS in the MAKE/MINE/Makefile.[machine], then compile and install as above.

When running:

  • Set OMP_NUM_THREADS environment variable for number of OpenMP threads per task, and

  • add LAMMPS_header_extra='package omp 0' input file argument.

  • Use LAMMPS pair style that activates omp, e.g. pair_style lj/cut/omp 3.00.

  • Pass flags to mpirun to tell it to run fewer MPI tasks than total number of cores assigned to entire job so that cores are available for OpenMP parallelization.

  • Example for OpenMPI, on 8 nodes, with 16 cores each, OpenMP parallelizing each MPI task’s LAMMPS work over all 16 cores:

    • export OMP_NUM_THREADS=16

    • mpirun -np 8 -x OMP_NUM_THREADS --map-by slot:pe=$OMP_NUM_THREADS ns_run < inputs

Note: the -np 8 may not be needed, depending on your queueing system.

Other notes

The LAMMPS ASE interface (ns_run_dir/lammpslib.py) is a heavily modified version of

https://svn.fysik.dtu.dk/projects/ase-extra/trunk/ase/calculators/lammpslib.py

For more information on how the interface works, see the lammpslib module.

For versions of mpi4py older than 2.0.0

If you have mpi4py version older than 2.0.0, you will need to patch LAMMPS as follows.

Apply the communicator patch to the LAMMPS source by doing

  • cd lammps_top_dir/src

  • patch < ns_run_dir/lammps_patches/communicator_self.patch

where ns_run_dir is the directory where ns_run is, and lammps_top_dir is the LAMMPS directory. Create a Makefile for parallel lammps in lammps_top_dir/src/MAKE. Define -DLIBRARY_MPI_COMM_WORLD=MPI_COMM_SELF in the LMP_INC makefile variable, then compile as above.

For older versions of LAMMPS

Important note: Check the lammps.py file as the path definition used to have a bug in the line:

else: self.lib = CDLL(join(modpath,"/liblammps_%s.so" % name),RTLD_GLOBAL)

You HAVE TO delete the / before liblammps otherwise it is interpreted as an absolute path!!!

Running

To start a nested sampling run type

ns_run < input

When running, it is strongly recommendded you set the OMP_NUM_THREADS=1 environment variable (e.g. in your jobscript) to avoid multiple OpenMP threads starting which can seriosly slow down the calculations (unless you have compiled LAMMPS to be used with mixed MPI-OpenMP).

Example input files can be found in the folder ./example_inputs.

For further help see also

ns_run --help

If you get weird errors about modules and/or .so files not found, do (in sh syntax)

export PYTHONPATH=ns_run_dir:$PYTHONPATH

where ns_run_dir is the directory where ns_run is. This appears to be necessary on some HPC machines where mpirun copies the executable, because ns_run by default looks for modules in the same directory as the top level python script itself. If it is still not sufficient, you might have to copy the entire ns_run_dir to the directory where the jobs are submitted from.

Running on ARCHER (UK National Supercomputing Service)

Install the latest ASE (3.9 or later) version and add that directory to your PYTHONPATH, as the default version on ARCHER is just 3.8.

Copy the whole pymatnest library to your /work directory, otherwise the compute nodes will not be able to read all the relevant python files.

In the job script you have to swap and load appropriate modules.

module load python-compute

module load pc-numpy

module load gcc

Analysis

To analyse the results you can use

ns_analyse -M 0.01 -D 0.01 -n 100 file.energies > analysis

For further help see also

ns_analyse --help

Temperature averaged analysis workflow

This assumes that QUIP is installed (structure_analysis_traj and mean_var_correl are part of QUIP).

Merge configurations using

ns_process_traj -t

Do analysis on output of ns_process_traj using structure_analysis_traj.

Add T-dependent weights to analyses using ns_set_analysis_weights. This will write new analysis files, one per temperature per analysis, with do_weights set in the header and each data line prepended by the weight.

Finally, use mean_var_correl to calculated the weighted mean of each analysis at each temperature.

Automatic script using QUIP ``ns_process_traj`` and ``structure_analysis_traj``:

make_thermal_average_xrd_rdfd_lenhisto.py is a script for calculating thermally averaged powder spectra ((...)_xrd), radial distribution functions ((...)_rdfd), which are currently disabled (see below), and histograms of lattice vector lengths ((...)_lattice_len_histo). RDFDs and XRDs are calculated for reference structures and safed under $STRUCTURE_NAME_V_mean_of_$TRAJ_signifpart_$SIGNIFICANT_PART.T_$T_xrd and $STRUCTURE_NAME_V_mean_of_$TRAJ_signifpart_$SIGNIFICANT_PART.T_$T_rdfd. It calculates the weights on its own and can deal with single trajectory files as well as combined trajectory files.

Before using, QUIP and quippy need to be installed and the variable QUIP_path in make_thermal_average_xrd_rdfd_lenhisto.py line 28 must be set to the QUIP build directory.

Important note: Only one script can be active in a single folder at a given time. Otherwise, temporary files will be overwritten and the results incorrect.

The script is called via:

python make_thermal_average_xrd_rdfd_lenhisto.py -fn traj.extxyz -Ts "600 800 1000" -nc 8 -nw 1920 -sn "bcc fcp hcp" -sc "test_struc_1.xyz test_struc_2.xyz

  • -fn is the file name. traj.extxyz can be a combined or a single trajectory.

  • -Ts are the different temperatures (which are transformed to integers) in the format “T_1 T_2 … T_N-1 T_N”.

  • -nc is the number of culled walkers per iteration.

  • -nw is the number of walkers.

  • -sn are the names of structures (defined in misc_calc_lib.py) for xrd spectrum identification in format ‘struc_name_1 struc_name_2 … struc_name_N-1 struc_name_N’. Only works for single species configurations.

  • -sc are the paths to the .extxyz/.xyz files of reference structures in format ‘path_1 path_2 … path_N-1 path_N’.

The following variables set in the script may be intersting:

significant_part

The parameter significant_part controls how much of the sampled structures we actually consider. It follows the name _signifpart_\ ``in the filename. For example, if it was set to 0.25 we would only consider the ca 25% most likely structures. (Due to discrete weight steps, this number is not exact.) The default value ofsignificant_part`` is 0.95. This ignores irrelevant structures and especially excludes high volume systems when we consider the solid phases. (To speed up the calculations one could go lower, but without further experimentation, no clear recommendations can be made with regards to this.)

do_rdfd

do_rdfd = False controls whether radial density functions are calculated. RDFs in QUIP are not using periodic cells. This makes it very hard to compare different cells of the same structure. Hence, it is turned off. If set to True, the script uses a 6x6x6 supercell for the comparison structures.

About the documentation

The documentation with example input files and a list of keywords…etc. can be found at https://libatoms.github.io/pymatnest/.

The documentation is generated by Sphinx, using the files within the doc library. Modules are autodocumented with .. automodule:: so all the properly formatted comments in the python code (i.e. within triple quote) appear. The installation and basic usage guidelines in the documentation are shown as the content of the README.md file is .. included:-d. Example inputs are located in the folder ./example_inputs and these files are also included in the documentation together with additional comments.