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一种使用键序电荷密度矩阵学习势能面的低阶置换不变多项式方法:应用于\(n = 3 - 10, 20\)的碳簇

A Low-Order Permutationally Invariant Polynomial Approach to Learning Potential Energy Surfaces Using the Bond-Order Charge-Density Matrix: Application to C Clusters for = 3-10, 20.

作者信息

Gutierrez-Cardenas Jose, Gibbas Benjamin D, Whitaker Kyle, Kaledin Martina, Kaledin Alexey L

机构信息

Department of Chemistry & Biochemistry, Kennesaw State University, 370 Paulding Ave NW ,Box#1203,Kennesaw 30144, Georgia.

Cherry L. Emerson Center for Scientific Computation and Department of Chemistry, Emory University, 1515 Dickey Drive ,Atlanta 30322, Georgia.

出版信息

J Phys Chem A. 2024 Sep 12;128(36):7703-7713. doi: 10.1021/acs.jpca.4c04281. Epub 2024 Aug 28.

DOI:10.1021/acs.jpca.4c04281
PMID:39205486
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11407436/
Abstract

A representation for learning potential energy surfaces (PESs) in terms of permutationally invariant polynomials (PIPs) using the Hartree-Fock expression for electronic energy is proposed. Our approach is based on the one-electron core Hamiltonian weighted by the configuration-dependent elements of the bond-order charge density matrix (CDM). While the previously reported model used an -function Gaussian basis for the CDM, the present formulation is expanded with -functions, which are crucial for describing chemical bonding. Detailed results are demonstrated on linear and cyclic C clusters ( = 3-10) trained on extensive B3LYP/aug-cc-pVTZ data. The described method facilitates PES learning by reducing the root mean squared error (RMSE) by a factor of 5 relative to the -function formulation and by a factor of 20 relative to the conventional PIP approach. This is equivalent to using CDM and an basis with a PIP of order to achieve the same RMSE as with the conventional method with a PIP of order + 2. Implications for large-scale problems are discussed using the case of the PES of the C fullerene in full permutational symmetry.

摘要

提出了一种使用电子能量的哈特里 - 福克表达式,根据置换不变多项式(PIP)来学习势能面(PES)的方法。我们的方法基于单电子核心哈密顿量,该哈密顿量由键序电荷密度矩阵(CDM)的构型相关元素加权。虽然先前报道的模型对CDM使用了 - 函数高斯基,但本公式使用 - 函数进行了扩展,这对于描述化学键至关重要。在基于广泛的B3LYP/aug - cc - pVTZ数据训练的线性和环状C簇( = 3 - 10)上展示了详细结果。所描述的方法通过将均方根误差(RMSE)相对于 - 函数公式降低5倍,相对于传统PIP方法降低20倍,促进了PES学习。这相当于使用CDM和一个 基,其PIP阶数为 ,以实现与传统方法PIP阶数为 + 2时相同的RMSE。使用具有完全置换对称性的C富勒烯的PES情况讨论了对大规模问题产生的影响。

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2
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Phys Chem Chem Phys. 2024 Apr 24;26(16):12324-12330. doi: 10.1039/d3cp05756a.
3
Degeneration of kernel regression with Matern kernels into low-order polynomial regression in high dimension.
在高维情况下,具有马特恩核的核回归退化为低阶多项式回归。
J Chem Phys. 2024 Jan 14;160(2). doi: 10.1063/5.0187867.
4
On-surface synthesis of aromatic cyclo[10]carbon and cyclo[14]carbon.表面合成芳香[10]环碳和[14]环碳。
Nature. 2023 Nov;623(7989):972-976. doi: 10.1038/s41586-023-06741-x. Epub 2023 Nov 29.
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