Permutationally Restrained Diabatization by Machine Intelligence

Autor(es)

Shu, Yinan ^{[1, 2]} ; Varga, Zoltan ^{[1, 2]} ; Sampaio de Oliveira-Filho, Antonio Gustavo ^[3] ; Truhlar, Donald G. ^{[2, 4]}

Afiliação do(s) autor(es)

^[1] Univ Minnesota, Dept Chem, Minneapolis, MN 55455 - USA

^[2] Univ Minnesota, Supercomp Inst, Minneapolis, MN 55455 - USA

^[3] Univ Sao Paulo, Dept Quim, Lab Computac Espectroscopia & Cinet, Fac Filosofia Ciencias & Letras Ribeirao Preto, BR-14040901 Ribeirao Preto, SP - Brazil

^[4] Univ Minnesota, Dept Chem, Chem Theory Ctr, Minneapolis, MN 55455 - USA

• Tipo de documento: Artigo Científico
• Fonte: JOURNAL OF CHEMICAL THEORY AND COMPUTATION; v. 17, n. 2, p. 1106-1116, FEB 9 2021.
• Citações Web of Science: 0

Resumo

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, non-symmetry-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. (AU)

• Processo FAPESP: 20/08553-2 - Espectroscopia computacional de moléculas diatômicas
• Beneficiário: Antonio Gustavo Sampaio de Oliveira Filho
• Modalidade de apoio: Auxílio à Pesquisa - Regular