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.
@article{arxiv.2009.14470,
title = {EZFF: Python Library for Multi-Objective Parameterization and Uncertainty Quantification of Interatomic Forcefields for Molecular Dynamics},
author = {Aravind Krishnamoorthy and Ankit Mishra and Deepak Kamal and Sungwook Hong and Ken-ichi Nomura and Subodh Tiwari and Aiichiro Nakano and Rajiv Kalia and Rampi Ramprasad and Priya Vashishta},
journal= {arXiv preprint arXiv:2009.14470},
year = {2021}
}