.png){kind=icon}
返回
Permutationally Restrained Diabatization by Machine Intelligence
.png){kind=icon}
PRE
AI
DOI：10.1021/acs.jctc.0c01110.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
期刊
IF:
5.5
论文数:
1.1W
被引数:
5.4W
机构
引用论文
.png){kind=icon}
Self-assembly of five new organic–inorganic hybrids based on two new flexible tricationic templates基于两个新的柔性三阳离子模板的五个新的有机-无机杂化物的自组装
PREAI
分享收藏分享收藏Permutation invariant polynomial neural network approach to fitting potential energy surfaces. II. Four-atom systems置换不变多项式神经网络方法拟合势能面。二。四原子系统
PREAI
分享收藏Permutation invariant polynomial neural network approach to fitting potential energy surfaces置换不变多项式神经网络方法拟合势能面
PREAI
分享收藏
查看更多内容