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Interatomic Potential Data and Simulations

Summary

This project provides resources to address some of the challenges to the wider use of classical atomistic simulations (e.g. molecular dynamics and Monte-Carlo). This is done through the following activities:

  • Curating implementations of classical interatomic potentials (force fields) for users to discover.
  • Evaluating thermodynamic, structural, and kinetic property predictions to characterize how the different hosted models behave under different conditions.
  • Hosting workshops on "Atomistic Simulations for Industrial Needs" to understand and address challenges to industrial use of these methods.
  • Developing tools for designing reproducible atomistic calculations.
  • Collaborating with the wider community to facilitate the development of common formats and evaluation protocols for atomistic simulations.
 
 

Description

Interatomic Potential Repository

IPR logo

The Interatomic Potentials Repository (IPR)  provides a source for interatomic potentials (force fields), related files, and evaluation tools to help researchers obtain interatomic models and judge their quality and applicability. The files provided are of known provenance and have either been submitted or vetted by their developers or were obtained from other trusted databases. Interatomic potentials and/or related files are currently available for various metals, semiconductors, oxides, and carbon-containing systems.

All content found on the Interatomic Potentials Repository is included in the CDCS database hosted at potentials.nist.gov. The content can be accessed, explored, and downloaded by going to potentials.nist.gov, or by using the potentials Python package available on Github. The potentials package also includes extra tools supporting the use and construction of the hosted parameter files.

Atomistic Manipulation Toolkit (atomman)

The atomman Python package contains tools for constructing and analyzing atomic configurations, with a focus on crystalline defects. It is meant to facilitate the rapid design and development of simulations that are fully documented and easily adaptable to new potentials, configurations, etc. The underlying configuration representation is made general to better support both small- and large-scale configurations, with conversions to many known formats.

iprPy High-Throughput Framework 

The iprPy Python package collects complete atomistic calculation methods for performing the property evaluations hosted by the Interatomic Potentials Repository and contains tools for running high-throughput workflows of those calculations. The included calculations are designed with a focus on reproducibility and transparency of the methods.

JARVIS-FF 

JARVIS

The JARVIS-FF (force fields) database is a collection of LAMMPS calculation-based data covering crystal structure, formation energy, phonon density of states, band structure, surface energy and defect formation energy. It is designed to help selecting the optimal FF for the user’s application. Currently the database contains ~110 FFs and ~1500 materials. For each material, the initial crystal structures are obtained from the JARVIS-DFT database, then automatically converted into LAMMPS format inputs and optimized, before performing the LAMMPS calculations to produce the afore mentioned properties. When possible, these properties are compared to corresponding DFT data, to help evaluate the quality of the force-fields for a specific application

Physically-informed Neural Network (PINN) potentials

Physicalyl Informed Neural Network Potentials
Credit: James Hickman

PINN potentials represent a novel method for describing bonding forces between atoms. The method combines the transferability of physically derived analytic models with the flexibility and accuracy of neural networks. Currently efforts are underway at NIST to develop PINN potentials for several chemical systems including Si, Cu, Pt, Al, and Ge.

 

PYFIT-FF

PyFit-FF logo

PYFIT-FF is a python package for training artificial neural network interatomic potentials (including PINN potentials). This is done using the PyTorch optimization library which is highly optimized for accelerated computation via CPU or GPU hyper-threading. The main benefits of PYFIT is that it is simple, highly portable, computationally efficient, flexible, well documented, and open source.  

 

 

Created February 10, 2020, Updated July 28, 2020