EZFF: Python Library for Multi-Objective Parameterization and Uncertainty Quantification of Interatomic Forcefields for Molecular Dynamics

TL;DR

EZFF is a Python library that simplifies the complex process of parameterizing interatomic forcefields in molecular dynamics, enabling multi-objective optimization, uncertainty quantification, and easy extension to various forcefield types and MD engines.

Contribution

It introduces a lightweight, flexible Python tool that integrates genetic algorithms for multi-objective parameterization and uncertainty quantification of interatomic forcefields in molecular dynamics.

Findings

Supports hybrid forcefield parameterization

Provides built-in uncertainty quantification

Easily extendable to other forcefield forms and MD engines

Abstract

Parameterization of interatomic forcefields is a necessary first step in performing molecular dynamics simulations. This is a non-trivial global optimization problem involving quantification of multiple empirical variables against one or more properties. We present EZFF, a lightweight Python library for parameterization of several types of interatomic forcefields implemented in several molecular dynamics engines against multiple objectives using genetic-algorithm-based global optimization methods. The EZFF scheme provides unique functionality such as the parameterization of hybrid forcefields composed of multiple forcefield interactions as well as built-in quantification of uncertainty in forcefield parameters and can be easily extended to other forcefield functional forms as well as MD engines.