pafi 0.9.9

PAFI: A Python package for the evaluation of free energy barriers beyond Harmonic TST using LAMMPS

PAFI: Evaluation of free energy barriers beyond Harmonic TST

Swinburne and Marinica, Phys. Rev. Lett 2018 (bibtex citation).

PAFI performs constrained sampling on NEB hyperplanes in LAMMPS, analytically reformulating an exact expression for the free energy gradient used in the Adaptive Biasing Force method. This allows calculation of free energy barriers even when the minimum energy path (MEP) is not aligned with the minimum free energy path (MFEP). PAFI thus performs stratified sampling of configuration space for a particular metastable pathway, with the usual reductions in variance.

Installation | Running PAFI | Plotting Results | Hints and tips | Citation / PAFI studies

Installation

If you can already run

from mpi4py import MPI
from lammps import lammps
lmp = lammps()
lmp.close()

Then you can install PAFI:

pip install pafi
pafi-check-deps
pafi-run-tests

We can also use conda-lammps, but this limits us to one CPU/worker

conda config --add channels conda-forge # add conda-forge channel
conda create -n pafi-env python=3.10 
conda activate pafi-env 
conda install numpy scipy pandas 
conda install mpi4py lammps 
pip install pafi

# ensure lammps can be loaded
conda activate pafi-env 
pafi-check-deps 

# testing
pip install pytest
pafi-run-tests # run tests

For best performance on HPC, please see here for detailed installation instructions.

Running PAFI

See here for PAFI example scripts.

PAFI requires that you have already made some NEB calculation with some potential. You can then run

  mpirun -np ${NUM_PROCS} python simple_pafi_run.py

simple_pafi_run.py:

  from mpi4py import MPI
  from pafi import PAFIManager, PAFIParser

  parameters = PAFIParser()
  parameters.set_potential(["neb_pathway/Fe.eam.fs"], # List of potential files
                                            "eam/fs", # LAMMPS pair style
                                             ["Fe"])  # List of Elements
  
  parameters.set_pathway("neb_pathway/image_*.dat") # NEB pathway of LAMMPS dat files
  

  parameters.axes["Temperature"] = [100.*i for i in range(7)] # temperature range
  parameters.set("CoresPerWorker",1) # Will force to 1 for conda installation of lammps
  
  # Typical production values
  parameters.set("SampleSteps",2000) # Sampling steps per worker
  parameters.set("ThermSteps",1000) # Thermalization steps per worker
  parameters.set("ThermWindow",500) # Averaging window to check temperature
  parameters.set("nRepeats",1) # Number of times to repeat process (if CPU limited)

  # Establish PAFIManager
  manager = PAFIManager(MPI.COMM_WORLD,parameters=parameters)
  manager.run()
  manager.close()

This will return a *.csv file with data readable by pandas. Can easily collate multiple runs.

Plotting Results

We can load in multiple csv files from some run then plot in python:

import matplotlib.pyplot as plt
from glob import glob
from pafi import ResultsProcessor

p = ResultsProcessor(   data_path=glob("dumps/pafi_data_*.csv"),
                        xml_path='dumps/config_0.xml', integrate=True)

plotting_data,x_key,y_key = p.plotting_data()


# Plotting
fig = plt.figure(figsize=(5,3))
ax = fig.add_subplot(111)
ax.set_title("Short (10ps) test for vacancy in W, EAM potential")


for i,row in enumerate(plotting_data):
    # Plotting data    
    x,y,e = row[x_key], row[y_key], row[y_key+"_err"]
    T = int(row['Temperature'])
    label = r"$\Delta\mathcal{F}$=%2.2f±%2.2f eV, T=%dK"% (y.max(),e.max(),T)
    
    # make plots
    ax.fill_between(x,y-e,y+e,facecolor=f'C{i}',alpha=0.5)
    ax.plot(x,y,f'C{i}-',lw=2,label=label)

# save
ax.legend(loc='best')
ax.set_xlabel("Reaction coordinate")
ax.set_ylabel("Free energy barrier (eV)")

See the examples and hints and tips for more information

Hints and Tips

• See LAMMPS NEB for making a pathway

• New equal style gives optimal spacing for force integration- e.g. fix neb all neb 1.0 parallel equal

• Modify one of examples/configuration_files/*_REAL.xml to load in your pathway and potential:

  from mpi4py import MPI
  from pafi import PAFIManager
  manager = PAFIManager(MPI.COMM_WORLD,"/path/to/config.xml")
  manager.run()
  manager.close()

• See the tutorial for information on the pafi-path-test routine

• In general, we want a reference pathway with dense discretisation where energy gradients are large

• The current non-smoothed spline implementation can oscillate between very similar image configurations, as a result, there should be non-negligible displacement between images

• If your path isn't loading, try setting LogLammps=1 in config.xml to check for bugs in log.lammps

• If SampleSteps is too large workers will make thermally activated "jumps" to nearby paths in the hyperplane. This will return a warning message Reference path too unstable for sampling. and increase error. If this happens, decrease SampleSteps and increase nRepeats

• When running on NPROCS cores, we require NPROCS%CoresPerWorker==0, so we have an integer number of workers

• The total number of force calls per worker is nPlanes * (ThermSteps+SampleSteps) * nRepeats, spatially parallelised by LAMMPS across CoresPerWorker cores for each worker.

• Each PAFI worker runs at the same speed as LAMMPS. Increasing CoresPerWorker will typically decrease execution time but also reduce nWorkers and increase error, as we have less samples.

• If you are core-limited, the nRepeats option forces workers to perform multiple independent sampling runs on each plane. For example, with all other parameters fixed, running on 32 cores with nRepeats=3 is equivalent to running on 3*32=96 cores with nRepeats=1, but the latter will finish in a third of the time.

Citation

For more details please see

Swinburne and Marinica, Unsupervised Calculation of Free Energy Barriers in Large Crystalline Systems, Phys. Rev. Lett., 2018 link

Citation:

@article{PhysRevLett.120.135503,
  title = {Unsupervised Calculation of Free Energy Barriers in Large Crystalline Systems},
  author = {Swinburne, Thomas D. and Marinica, Mihai-Cosmin},
  journal = {Phys. Rev. Lett.},
  volume = {120},
  issue = {13},
  pages = {135503},
  numpages = {6},
  year = {2018},
  month = {Mar},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevLett.120.135503},
  url = {https://link.aps.org/doi/10.1103/PhysRevLett.120.135503}
}

Some use cases of PAFI:

• Allera et al., Activation entropy of dislocation glide, arXiv, 2024 link
• Nahavandian et al., From anti-Arrhenius to Arrhenius behavior in a dislocation-obstacle bypass: Atomistic Simulations and Theoretical Investigation, Computational Materials Science, 2024 link
• Namakian et al., Temperature dependence of generalized stacking fault free energy profiles and dissociation mechanisms of slip systems in Mg, Computational Materials Science, 2024 link
• Namakian et al., Temperature dependent stacking fault free energy profiles and partial dislocation separation in FCC Cu, Computational Materials Science, 2023 link
• Baima et al., Capabilities and limits of autoencoders for extracting collective variables in atomistic materials science, Physical Chemistry Chemical Physics, 2022 link
• Sato et al., Anharmonic effect on the thermally activated migration of {101̄2} twin interfaces in magnesium, Materials Research Letters, 2021 link

TODO

1. Restart files from pathway deviations
2. Smoothed spline interpolation for more general reference pathways