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20242026
most citedThe Python Simulations of Chemistry Framework: 10 years of an open-source quantum chemistry project

4 citations · 5 across the 4 of their papers we have counts for

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physics.chem-ph2026

THEMol dataset: Torsion, Hessian, and Energy of Molecules

Jiashu Liang, Tianze Zheng, Yu Xia +13

We present THEMol (Torsion, Hessian, Energy of Molecules), a massive open-source collection of quantum mechanical properties tailored for closed-shell organic molecules, with up to…

physics.chem-ph20261 cited

TDDFT Gradients and Nonadiabatic Couplings with Minimal Auxiliary Basis Set Approximation for Fewest-Switches Surface Hopping Dynamics

Cheng Fan, Zhichen Pu, Zehao Zhou +3

The electronic structure calculations remain a major bottleneck in ab initio nonadiabatic molecular dynamics. We develop an efficient TDDFT-based FSSH implementation in the GPU4PyS…

physics.chem-ph20264 cited

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

Towards A Transferable Acceleration Method for Density Functional Theory

Zhe Liu, Yuyan Ni, Zhichen Pu +3

Recently, sophisticated deep learning-based approaches have been developed for generating efficient initial guesses to accelerate the convergence of density functional theory (DFT)…

physics.chem-ph2025

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids

Wei Feng, Siyuan Liu, Hongyi Wang +11

The thermal conductivity of organic liquids is a vital parameter influencing various industrial and environmental applications, including energy conversion, electronics cooling, an…

physics.chem-ph2025

Analytical Excited-State Gradients and Derivative Couplings in TDDFT with Minimal Auxiliary Basis Set Approximation and GPU Acceleration

Zhichen Pu, Xiaojie Wu, Yuanheng Wang +5

Calculating excited-state gradients and derivative couplings using time-dependent density functional theory (TDDFT) remains a computationally demanding task. An efficient variant,…