A perspective marking 20 years of using permutationally invariant polynomials for molecular potentials DOI
Joel M. Bowman, Chen Qu, Riccardo Conte

et al.

The Journal of Chemical Physics, Journal Year: 2025, Volume and Issue: 162(18)

Published: May 13, 2025

This Perspective is focused on permutationally invariant polynomials (PIPs). Since their introduction in 2004 and first use developing a fully potential for the highly fluxional cation CH5+, PIPs have found widespread machine learned potentials (MLPs) isolated molecules, chemical reactions, clusters, condensed phase, materials. More than 100 been reported using PIPs. The popularity of MLPs stems from fundamental property being with respect to permutations like atoms; this energy surfaces. achieved global descriptors and, thus, without an atom-centered approach (which manifestly invariant). used directly linear regression fitting electronic energies gradients complex landscapes reactions numerous product channels. also as inputs neural network Gaussian process methods many-body (atom-centered, water monomer, etc.) applications, notably gold standard water. Here, we focus progress usage since 2018, when last review was done by our group.

Language: Английский

A perspective marking 20 years of using permutationally invariant polynomials for molecular potentials DOI
Joel M. Bowman, Chen Qu, Riccardo Conte

et al.

The Journal of Chemical Physics, Journal Year: 2025, Volume and Issue: 162(18)

Published: May 13, 2025

This Perspective is focused on permutationally invariant polynomials (PIPs). Since their introduction in 2004 and first use developing a fully potential for the highly fluxional cation CH5+, PIPs have found widespread machine learned potentials (MLPs) isolated molecules, chemical reactions, clusters, condensed phase, materials. More than 100 been reported using PIPs. The popularity of MLPs stems from fundamental property being with respect to permutations like atoms; this energy surfaces. achieved global descriptors and, thus, without an atom-centered approach (which manifestly invariant). used directly linear regression fitting electronic energies gradients complex landscapes reactions numerous product channels. also as inputs neural network Gaussian process methods many-body (atom-centered, water monomer, etc.) applications, notably gold standard water. Here, we focus progress usage since 2018, when last review was done by our group.

Language: Английский

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