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Analytical Gradients for Molecular-Orbital-Based Machine Learning
Sebastian J. R. Lee, Tamara Husch, Feizhi Ding +1
Molecular-orbital-based machine learning (MOB-ML) enables the prediction of accurate correlation energies at the cost of obtaining molecular orbitals. Here, we present the derivati…
Improved accuracy and transferability of molecular-orbital-based machine learning: Organics, transition-metal complexes, non-covalent interactions, and transition states
Tamara Husch, Jiace Sun, Lixue Cheng +2
Molecular-orbital-based machine learning (MOB-ML) provides a general framework for the prediction of accurate correlation energies at the cost of obtaining molecular orbitals. We d…
Semiempirical Molecular Orbital Models based on the Neglect of Diatomic Differential Overlap Approximation
Tamara Husch, Alain C. Vaucher, Markus Reiher
Semiempirical molecular orbital (SEMO) models based on the neglect of diatomic differential overlap (NDDO) approximation efficiently solve the self-consistent field equations by ra…
Comprehensive Analysis of the Neglect of Diatomic Differential Overlap Approximation
Tamara Husch, Markus Reiher
Many modern semiempirical molecular orbital models are built on the neglect of diatomic differential overlap (NDDO) approximation. An in-depth understanding of this approximation i…