Gutierrez-Cardenas Jose, Gibbas Benjamin D, Whitaker Kyle, Kaledin Martina, Kaledin Alexey L
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.
J Phys Chem A. 2024 Sep 12;128(36):7703-7713. doi: 10.1021/acs.jpca.4c04281. Epub 2024 Aug 28.
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.
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。