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20192026
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physics.chem-ph2026

Learning to Rank for Selected Configuration Interaction

Wan Nie, Songwei Liu, Yingying Yu +2

The accurate description of electron correlation is a central challenge in computational chemistry, with selected configuration interaction (SCI) emerging as a powerful tool to app…

physics.chem-ph2026

Multi-GPU MBE(3)-OSV-MP2 for Performant Large-Scale ab initio Calculations

Qiujiang Liang, Jun Yang

The computational acceleration of orbital-invariant local correlation methods on graphics processing units (GPUs) has remained largely unexplored due to substantial algorithmic com…

physics.chem-ph2024

Polarizable Water Model with Ab Initio Neural Network Dynamic Charges and Spontaneous Charge Transfer

Qiujiang Liang, Jun Yang

Simulating water accurately has been a challenge due to the complexity of describing polarization and intermolecular charge transfer. Quantum mechanical (QM) electronic structures…

physics.chem-ph2023

Low-data deep quantum chemical learning for accurate MP2 and coupled-cluster correlations

Wai-Pan Ng, Qiujiang Liang, Jun Yang

Accurate ab-initio prediction of electronic energies is very expensive for macromolecules by explicitly solving post-Hartree-Fock equations. We here exploit the physically justifie…

physics.chem-ph2019

A complete OSV-MP2 analytical gradient theory for molecular structure and dynamics simulations

Ruiyi Zhou, Qiujiang Liang, Jun Yang

We propose an exact algorithm for computing the analytical gradient within the framework of the orbital-specific-virtual (OSV) second-order Møller-Plesset (MP2) theory in resolutio…