Installation and quick start guide

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 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 QUIP

Make sure your PYTHONPATH is set correctly to find the quippy module. (Note: Check the QUIP_ARCH with which you build quippy, as some might result in segmentation fault when running ns_run (e.g. try gfortan_openmpi if gfortran fails)

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.

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

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.

### Nearly mandatory compilation flags

It is extremely useful, essentially required, to take advantage of the (relatively recent) -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

### 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 polymers

Copy the four bond and angle 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

You have to compile a parallel version of LAMMPS. LAMMPS “serial” compilation still links to fake MPI routines, which then conflict in unpredictable ways with the true mpi routines that mpi4py includes.

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

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

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.

About the documentation

The documentation with example input files and a list of keywords…etc. can be found at <http://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.