activity
20242026
most citedExploring the design space of machine-learning models for quantum chemistry with a fully differentiable framework

14 citations · 36 across the 13 of their papers we have counts for

collaborators
Showing physics.chem-phShow all

8 papers · 1 filter

physics.chem-ph2026

The Python Simulations of Chemistry Framework: 10 years of an open-source quantum chemistry project

Qiming Sun, Matthew R Hermes, Xiaojie Wu +100

Over the past decade, the Python-based Simulations of Chemistry Framework (PySCF) has developed into a widely used open-source platform for electronic structure theory and quantum…

physics.chem-ph2026

Implementation of the multigrid Gaussian-Plane-Wave algorithm with GPU acceleration in PySCF

Rui Li, Xing Zhang, Qiming Sun +3

We introduce a GPU-accelerated multigrid Gaussian-Plane-Wave density fitting (FFTDF) approach for efficient Fock builds and nuclear gradient evaluations within Kohn-Sham density fu…

physics.chem-ph2025

Predictive Free Energy Simulations Through Hierarchical Distillation of Quantum Hamiltonians

Chenghan Li, Garnet Kin-Lic Chan

Obtaining the free energies of condensed phase chemical reactions remains computationally prohibitive for high-level quantum mechanical methods. We introduce a hierarchical machine…

physics.chem-ph2025

Reactive Chemistry at Unrestricted Coupled Cluster Level: High-throughput Calculations for Training Machine Learning Potentials

Alice E. A. Allen, Rui Li, Sakib Matin +8

Accurately modeling chemical reactions at the atomistic level requires high-level electronic structure theory due to the presence of unpaired electrons and the need to properly des…

physics.chem-ph2025

Accurate crystal field Hamiltonians of single-ion magnets at mean-field cost

Linqing Peng, Shuanglong Liu, Xing Zhang +4

The effective crystal field Hamiltonian provides the key description of the electronic properties of single-ion magnets, but obtaining its parameters from ab initio computation is…

physics.chem-ph202514 cited

Exploring the design space of machine-learning models for quantum chemistry with a fully differentiable framework

Divya Suman, Jigyasa Nigam, Sandra Saade +5

Traditional atomistic machine learning (ML) models serve as surrogates for quantum mechanical (QM) properties, predicting quantities such as dipole moments and polarizabilities, di…