Charge-Optimized Electrostatic Interaction Atom-Centered Neural Network Algorithm

Song, Zichen1,2; Han, Jian2; Henkelman, Graeme3,4; Li, Lei1

2024-02-21

10.1021/acs.jctc.3c01254

JOURNAL OF CHEMICAL THEORY AND COMPUTATION   影响因子和分区

1549-9618

1549-9626

Machine-learning algorithms have been proposed to capture electrostatic interactions by using effective partial charges. These algorithms often rely on a pretrained model for partial charge prediction using density functional theory-calculated partial charges as references, which introduces complexity to the force field model. The accuracy of the trained model also depends on the reliability of charge partition methods, which can be dependent on the specific system and methodology employed. In this study, we propose an atom-centered neural network (ANN) algorithm that eliminates the need for reference charges. Our algorithm requires only a single NN model for each element to obtain both atomic energy and charges. These atomic charges are then employed to compute electrostatic energies using the Ewald summation algorithm. Subsequently, the force field model is trained on total energy and forces, with the inclusion of electrostatic energy. To evaluate the performance of our algorithm, we conducted tests on three benchmark systems, including a Ge slab with an O adatom system, a TiO2 crystalline system, and a Pd-O nanoparticle system. Our results demonstrate reasonably accurate predictions of partial charges and electrostatic interactions. This algorithm provides a self-consistent charge prediction strategy and possibilities for robust and reliable modeling of electrostatic interactions in machine-learning potentials.

SCI

英语

第一 ; 通讯

Division of Chemistry[92270103] ; Training Program of the Major Research Plan of the National Natural Science Foundation of China[JCYJ20210324115809026] ; Shenzhen fundamental research funding[ZDSYS20210709112802010] ; Center for Computational Science and Engineering of Southern University of Science and Technology[CHE-2102317] ; NSF[F-1841]

Chemistry ; Physics

Chemistry, Physical ; Physics, Atomic, Molecular & Chemical

WOS:001168280400001

AMER CHEMICAL SOC

Web of Science

1.Southern Univ Sci & Technol, Dept Mat Sci & Engn, Shenzhen Key Lab Micro Nanoporous Funct Mat SKLPM, Shenzhen 518055, Peoples R China
2.City Univ Hong Kong, Dept Mat Sci & Engn, Kowloon, Hong Kong, Peoples R China
3.Univ Texas Austin, Dept Chem, Austin, TX 78712 USA
4.Univ Texas Austin, Inst Computat Engn & Sci, Austin, TX 78712 USA

Song, Zichen,Han, Jian,Henkelman, Graeme,et al. Charge-Optimized Electrostatic Interaction Atom-Centered Neural Network Algorithm[J]. JOURNAL OF CHEMICAL THEORY AND COMPUTATION,2024,20(5).

Song, Zichen,Han, Jian,Henkelman, Graeme,&Li, Lei.(2024).Charge-Optimized Electrostatic Interaction Atom-Centered Neural Network Algorithm.JOURNAL OF CHEMICAL THEORY AND COMPUTATION,20(5).

Song, Zichen,et al."Charge-Optimized Electrostatic Interaction Atom-Centered Neural Network Algorithm".JOURNAL OF CHEMICAL THEORY AND COMPUTATION 20.5(2024).