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arXiv · 2009.14470

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

Aravind Krishnamoorthy·Ankit Mishra·Deepak Kamal·Sungwook Hong·Ken-ichi Nomura·Subodh Tiwari·Aiichiro Nakano·Rajiv Kalia·Rampi Ramprasad·Priya Vashishta
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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.

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Aravind Krishnamoorthy, Ankit Mishra, Deepak Kamal, Sungwook Hong, Ken-ichi Nomura, Subodh Tiwari, Aiichiro Nakano, Rajiv Kalia, Rampi Ramprasad, Priya Vashishta. 2020-09-30. EZFF: Python Library for Multi-Objective Parameterization and Uncertainty Quantification of Interatomic Forcefields for Molecular Dynamics. https://doi.org/10.1016/j.softx.2021.100663

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