SIMPLE-NN: An efficient package for training and executing neural-network interatomic potentials

Published: 24 May 2019| Version 1 | DOI: 10.17632/pjv2yr7pvr.1


The molecular dynamics (MD) simulation is a favored method in materials science for understanding and predicting material properties from atomistic motions. In classical MD simulations, the interaction between atoms is described by an empirical interatomic potential, so the reliability of the simulation hinges on the accuracy of the underlying potential. Recently, machine learning (ML) based interatomic potentials are gaining attention as they can reproduce potential energy surfaces (PES) of ab initio calculations, with a much lower computational cost. Therefore, an efficient code for training ML potentials and inferencing PES in new configurations would widen the application range of MD simulations. Here, we announce an open-source package, SNU Interatomic Machine-learning PotentiaL packagE-version Neural Network (SIMPLE-NN) that generates and utilizes the ML potential based on the artificial neural network with the Behler–Parrinello type symmetry function as descriptors for the chemical environments. SIMPLE-NN uses the Atomic Simulation Environment (ASE) package and Google Tensorflow for high expandability and efficient training, and also supports the in-house code for quasi-Newton method. Notably, the package features a weighting scheme based on the Gaussian density function (GDF), which significantly improves accuracy and reliability of ML potentials by resolving sampling bias that exists in typical training sets. For MD simulations, SIMPLE-NN interfaces with the LAMMPS package. We demonstrate the performance and usage of SIMPLE-NN with examples of SiO2.



Computational Physics, Machine Learning, Molecular Dynamics, Potential Energy Surface