Permutationally Restrained Diabatization by Machine Intelligence

Yinan Shu, Zoltan Varga, Antonio Gustavo Sampaio De Oliveira-Filho, Donald G. Truhlar

Research output: Contribution to journalArticlepeer-review

Abstract

Simulations of electronically nonadiabatic processes may employ either the adiabatic or diabatic representation. Direct dynamics calculations are usually carried out in the adiabatic basis because the energy, force, and state coupling can be evaluated directly by many electronic structure methods. However, although its straightforwardness is appealing, direct dynamics is expensive when combined with quantitatively accurate electronic structure theories. This generates interest in analytically fitted surfaces to cut the expense, but the cuspidal ridges of the potentials and the singularities and vector nature of the couplings at high-dimensional, nonsymmetry-determined intersections in the adiabatic representation make accurate fitting almost impossible. This motivates using diabatic representations, where the surfaces are smooth and the couplings are also smooth and-importantly-scalar. In a recent previous work, we have developed a method called diabatization by deep neural network (DDNN) that takes advantage of the smoothness and nonuniqueness of diabatic bases to obtain them by machine learning. The diabatic potential energy matrices (DPEMs) learned by the DDNN method yield not only diabatic potential energy surfaces (PESs) and couplings in an analytic form useful for dynamics calculations, but also adiabatic surfaces and couplings in the adiabatic representation can be calculated inexpensively from the transformation. In the present work, we show how to extend the DDNN method to produce good approximations to global permutationally invariant adiabatic PESs simultaneously with DPEMs. The extended method is called permutationally restrained DDNN.

Original languageEnglish (US)
Pages (from-to)1106-1116
Number of pages11
JournalJournal of Chemical Theory and Computation
Volume17
Issue number2
DOIs
StatePublished - Feb 9 2021

Bibliographical note

Funding Information:
The works of Y.S. and D.G.T. are supported in part by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under Award DE-SC0015997. The work of Z.V. is supported by the Air Force Office of Scientific Research under grant no. FA9550-19-1-0219. A.G.S.d.O.-F. thanks the São Paulo Research Foundation (FAPESP) (2020/08553-2), the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) of Brazil (306830/2018-3), and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.

Publisher Copyright:
© 2021 American Chemical Society. All rights reserved.

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