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20232026
most citedDeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

100 citations · 116 across the 32 of their papers we have counts for

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physics.chem-ph20251 cited

NMR-Solver: Automated Structure Elucidation via Large-Scale Spectral Matching and Physics-Guided Fragment Optimization

Yongqi Jin, Jun-Jie Wang, Fanjie Xu +6

Nuclear Magnetic Resonance (NMR) spectroscopy is one of the most powerful and widely used tools for molecular structure elucidation in organic chemistry. However, the interpretatio…

physics.chem-ph2025

Uni-Mol3: A Multi-Molecular Foundation Model for Advancing Organic Reaction Modeling

Lirong Wu, Junjie Wang, Zhifeng Gao +6

Organic reaction, the foundation of modern chemical industry, is crucial for new material development and drug discovery. However, deciphering reaction mechanisms and modeling mult…

physics.chem-ph2025100 cited

DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

Jinzhe Zeng, Duo Zhang, Anyang Peng +44

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for m…

physics.chem-ph2024

End-to-End Crystal Structure Prediction from Powder X-Ray Diffraction

Qingsi Lai, Fanjie Xu, Lin Yao +8

Powder X-ray diffraction (PXRD) is a prevalent technique in materials characterization. While the analysis of PXRD often requires extensive human manual intervention, and most auto…

physics.chem-ph2023

DPA-2: a large atomic model as a multi-task learner

Duo Zhang, Xinzijian Liu, Xiangyu Zhang +40

The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demo…