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.
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28 January 2023
Research Article|
January 24 2023
PESPIP: Software to fit complex molecular and many-body potential energy surfaces with permutationally invariant polynomials
Special Collection:
Software for Atomistic Machine Learning
Paul L. Houston
;
Paul L. Houston
a)
(Conceptualization, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing)
1
Department of Chemistry and Chemical Biology, Cornell University
, Ithaca, New York 14853, USA
and Department of Chemistry and Biochemistry, Georgia Institute of Technology
, Atlanta, Georgia 30332, USA
a)Author to whom correspondence should be addressed: plh2@cornell.edu
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Chen Qu
;
Chen Qu
b)
(Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing)
2
Independent Researcher
, Toronto, Ontario M9B0E3, Canada
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Qi Yu
;
Qi Yu
c)
(Investigation, Methodology, Software, Validation, Writing – original draft, Writing – review & editing)
3
Department of Chemistry, Yale University
, New Haven, Connecticut 06520, USA
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Riccardo Conte
;
Riccardo Conte
d)
(Conceptualization, Formal analysis, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing)
4
Dipartimento di Chimica, Università Degli Studi di Milano
, Via Golgi 19, 20133 Milano, Italy
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Apurba Nandi
;
Apurba Nandi
(Conceptualization, Formal analysis, Investigation, Methodology, Software, Validation, Writing – original draft, Writing – review & editing)
5
Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University
, Atlanta, Georgia 30322, USA
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Jeffrey K. Li;
Jeffrey K. Li
(Software, Validation)
5
Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University
, Atlanta, Georgia 30322, USA
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Joel M. Bowman
Joel M. Bowman
e)
(Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Project administration, Resources, Supervision, Validation, Writing – original draft, Writing – review & editing)
5
Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University
, Atlanta, Georgia 30322, USA
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a)Author to whom correspondence should be addressed: plh2@cornell.edu
b)
Electronic mail: szquchen@gmail.com
c)
Electronic mail: q.yu@yale.edu
d)
Electronic mail: riccardo.conte1@unimi.it
e)
Electronic mail: jmbowma@emory.edu
Note: This paper is part of the JCP Special Topic on Software for Atomistic Machine Learning.
J. Chem. Phys. 158, 044109 (2023)
Article history
Received:
November 10 2022
Accepted:
December 27 2022
Citation
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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