139 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Xiamen UniversityCN14 papers
- Fujian Normal UniversityCN8 papers
- Collaborative Innovation Center of Chemistry for Energy MaterialsCN6 papers
- Chinese Academy of SciencesCN4 papers
- Fuzhou UniversityCN4 papers
- Anhui UniversityCN2 papers
- Dalian University of TechnologyCN2 papers
- Fujian Institute of Research on the Structure of MatterCN2 papers
- Fujian Science and Technology Innovation Laboratory for Optoelectronic Information of ChinaCN2 papers
- Institute of Applied Physics and Computational MathematicsCN2 papers
- Institute of Technology of CambodiaKH2 papers
- National University of SingaporeSG2 papers
5 papers · 1 filter
Auto-resolving the atomic structure at van der Waals interfaces using a generative model
Wenqiang Huang, Yuchen Jin, Zhemin Li +12
The high-resolution visualization of atomic structures is significant for understanding the relationship between the microscopic configurations and macroscopic properties of materi…
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…
MLatom software ecosystem for surface hopping dynamics in Python with quantum mechanical and machine learning methods
Lina Zhang, Sebastian V. Pios, Mikołaj Martyka +7
We present an open-source MLatom@XACS software ecosystem for on-the-fly surface hopping nonadiabatic dynamics based on the Landau-Zener-Belyaev-Lebedev (LZBL) algorithm. The dynami…
Tell machine learning potentials what they are needed for: Simulation-oriented training exemplified for glycine
Fuchun Ge, Ran Wang, Chen Qu +6
Machine learning potentials (MLPs) are widely applied as an efficient alternative way to represent potential energy surfaces (PES) in many chemical simulations. The MLPs are often…
Physics-informed active learning for accelerating quantum chemical simulations
Yi-Fan Hou, Lina Zhang, Quanhao Zhang +2
Quantum chemical simulations can be greatly accelerated by constructing machine learning potentials, which is often done using active learning (AL). The usefulness of the construct…