MB-Fit: Software infrastructure for data-driven many-body potential energy functions

Bull-Vulpe, Ethan F.; Riera, Marc; Götz, Andreas W.; Paesani, Francesco

doi:10.1063/5.0063198

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MB-Fit: Software infrastructure for data-driven many-body potential energy functions

Bull-Vulpe, Ethan F.·Riera, Marc·Götz, Andreas W.·Paesani, Francesco

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Abstract

Many-body potential energy functions (MB-PEFs), which integrate data-driven representations of many-body short-range quantum mechanical interactions with physics-based representations of many-body polarization and long-range interactions, have recently been shown to provide high accuracy in the description of molecular interactions from the gas to the condensed phase. Here, we present MB-Fit, a software infrastructure for the automated development of MB-PEFs for generic molecules within the TTM-nrg (Thole-type model energy) and MB-nrg (many-body energy) theoretical frameworks. Besides providing all the necessary computational tools for generating TTM-nrg and MB-nrg PEFs, MB-Fit provides a seamless interface with the MBX software, a many-body energy and force calculator for computer simulations. Given the demonstrated accuracy of the MB-PEFs, particularly within the MB-nrg framework, we believe that MB-Fit will enable routine predictive computer simulations of generic (small) molecules in the gas, liquid, and solid phases, including, but not limited to, the modeling of quantum isomeric equilibria in molecular clusters, solvation processes, molecular crystals, and phase diagrams.

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY 71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS atomic and molecular clusters electronic structure methods liquids machine learning many body systems molecular dynamics molecular dynamics software molecular simulations quantum mechanical calculations thermodynamic properties

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Bull-Vulpe, Ethan F., Riera, Marc, Götz, Andreas W., Paesani, Francesco. 2021-09-23. MB-Fit: Software infrastructure for data-driven many-body potential energy functions. https://doi.org/10.1063/5.0063198

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