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
10 papers
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…
Learning-at-Criticality in Large Language Models for Quantum Field Theory and Beyond
Xiansheng Cai, Sihan Hu, Tao Wang +4
Fundamental physics often confronts complex symbolic problems with few guiding exemplars or established principles. While artificial intelligence (AI) offers promise, its typical n…
ChatCFD: An LLM-Driven Agent for End-to-End CFD Automation with Structured Knowledge and Reasoning
E Fan, Kang Hu, Zhuowen Wu +6
Computational Fluid Dynamics (CFD) is critical for scientific advancement but is hindered by operational complexity and high expertise barriers. This paper introduces ChatCFD, a La…
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…
Deep variational free energy prediction of dense hydrogen solid at 1200K
Xinyang Dong, Hao Xie, Yixiao Chen +4
We perform deep variational free energy calculations to investigate the dense hydrogen system at 1200 K and high pressures. In this computational framework, neural networks are use…
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…
Revealing the molecular structures of a-Al2O3(0001)-water interface by machine learning based computational vibrational spectroscopy
Xianglong Du, Weizhi Shao, Chenglong Bao +3
Solid-water interfaces are crucial to many physical and chemical processes and are extensively studied using surface-specific sum-frequency generation (SFG) spectroscopy. To establ…
Deep learning tight-binding approach for large-scale electronic simulations at finite temperatures with accuracy
Qiangqiang Gu, Zhanghao Zhouyin, Shishir Kumar Pandey +3
Simulating electronic behavior in materials and devices with realistic large system sizes remains a formidable task within the framework due to its computational inte…
of two-dimensional electron gas: a neural canonical transformation study
Hao Xie, Linfeng Zhang, Lei Wang
The quasiparticle effective mass of interacting electrons is a fundamental quantity in the Fermi liquid theory. However, the precise value of the effective mass of uniform…
A Tungsten Deep Neural-Network Potential for Simulating Mechanical Property Degradation Under Fusion Service Environment
XiaoYang Wang, YiNan Wang, LinFeng Zhang +2
Tungsten is a promising candidate material in fusion energy facilities. Molecular dynamics (MD) simulations reveal the atomistic scale mechanisms, so they are crucial for the under…