MB-Fit: Software Infrastructure for Data-Driven Many-Body Potential Energy Functions

16 July 2021, Version 2
This content is a preprint and has not undergone peer review at the time of posting.


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 auto- mated 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/force calculator for computer simulations. Given the demonstrated accuracy of the MB-PEFs, 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 isomeric equilibria in molecular clusters, solvation processes, molecular crystals, and phase diagrams.


machine learning
data-driven models
many-body interactions
electronic structure
permutationally invariant polynomials

Supplementary materials

Supplementary Material
Details on the implementation of the permutationally in- variant polynomials in MB-Fit.

Supplementary weblinks


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