EZFF: Python library for multi-objective parameterization and uncertainty quantification of interatomic forcefields for molecular dynamics

A Krishnamoorthy and A Mishra and D Kamal and S Hong and K Nomura and S Tiwari and A Nakano and R Kalia and R Ramprasad and P Vashishta, SOFTWAREX, 13, 100663 (2021).

DOI: 10.1016/j.softx.2021.100663

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. (C) 2021 The Authors. Published by Elsevier B.V.

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