We wish to describe a potential energy surface by using a basis of permutationally invariant polynomials whose coefficients will be determined by numerical regression so as to smoothly fit a dataset of electronic energies as well as, perhaps, gradients. The polynomials will be powers of transformed internuclear distances, usually either Morse variables, exp(−ri,j/λ), where λ is a constant range hyperparameter, or reciprocals of the distances, 1/ri,j. The question we address is how to create the most efficient basis, including (a) which polynomials to keep or discard, (b) how many polynomials will be needed, (c) how to make sure the polynomials correctly reproduce the zero interaction at a large distance, (d) how to ensure special symmetries, and (e) how to calculate gradients efficiently. This article discusses how these questions can be answered by using a set of programs to choose and manipulate the polynomials as well as to write efficient Fortran programs for the calculation of energies and gradients. A user-friendly interface for access to monomial symmetrization approach results is also described. The software for these programs is now publicly available.
PESPIP: Software to fit complex molecular and many-body potential energy surfaces with permutationally invariant polynomials
Note: This paper is part of the JCP Special Topic on Software for Atomistic Machine Learning.
Paul L. Houston, Chen Qu, Qi Yu, Riccardo Conte, Apurba Nandi, Jeffrey K. Li, Joel M. Bowman; PESPIP: Software to fit complex molecular and many-body potential energy surfaces with permutationally invariant polynomials. J. Chem. Phys. 28 January 2023; 158 (4): 044109. https://doi.org/10.1063/5.0134442
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