工作流程
偶极子
紧密结合
哈密顿量(控制论)
计算机科学
双原子分子
电子结构
基函数
统计物理学
机器学习
人工智能
计算化学
化学
数学
分子
物理
量子力学
数学优化
数据库
作者
Guozheng Fan,Adam McSloy,Bálint Aradi,ChiYung Yam,Thomas Frauenheim
标识
DOI:10.1021/acs.jpclett.2c02586
摘要
We have introduced a machine learning workflow that allows for optimizing electronic properties in the density functional tight binding method (DFTB). The workflow allows for the optimization of electronic properties by generating two-center integrals, either by training basis function parameters directly or by training a spline model for the diatomic integrals, which are then used to build the Hamiltonian and the overlap matrices. Using our workflow, we have managed to obtain improved electronic properties, such as charge distributions, dipole moments, and approximated polarizabilities. While both machine learning approaches enabled us to improve on the electronic properties of the molecules as compared with existing DFTB parametrizations, only by training on the basis function parameters we were able to obtain consistent Hamiltonians and overlap matrices in the physically reasonable ranges or to improve on multiple electronic properties simultaneously.
科研通智能强力驱动
Strongly Powered by AbleSci AI