100 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Chinese Academy of SciencesCN4 papers
- Peking UniversityCN4 papers
- Art Institute of PortlandUS3 papers
- Institute of Applied Physics and Computational MathematicsCN3 papers
- Collaborative Innovation Center of Chemistry for Energy MaterialsCN2 papers
- Princeton UniversityUS2 papers
- Tsinghua UniversityCN2 papers
- University of Chinese Academy of SciencesCN2 papers
- University of Science and Technology of ChinaCN2 papers
- Xiamen UniversityCN2 papers
- Asahi Kasei (Japan)JP1 paper
- Baidu (China)CN1 paper
3 papers · 1 filter
Mixture of experts architectures for machine learning interatomic potentials
Yuzhi Liu, Duo Zhang, Anyang Peng +3
Machine Learning Interatomic Potentials (MLIPs) enable accurate large-scale atomistic simulations, yet improving their expressive capacity efficiently remains challenging. Here we…
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…
On the design space between molecular mechanics and machine learning force fields
Yuanqing Wang, Kenichiro Takaba, Michael S. Chen +14
A force field as accurate as quantum mechanics (QM) and as fast as molecular mechanics (MM), with which one can simulate a biomolecular system efficiently enough and meaningfully e…