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
7 papers · 1 filter
Machine Learning Accelerated Computational Surface-Specific Vibrational Spectroscopy Reveals Oxidation Level of Graphene in Contact with Water
Xianglong Du, Jun Cheng, Fujie Tang
Precise characterization of the graphene/water interface has been hindered by experimental inconsistencies and limited molecular-level access to interfacial structures. In this wor…
Decoding the Competing Effects of Dynamic Solvation Structures on Nuclear Magnetic Resonance Chemical Shifts of Battery Electrolytes via Machine Learning
Qi You, Yan Sun, Feng Wang +2
Understanding the solvation structure of electrolytes is critical for optimizing the electrochemical performance of rechargeable batteries, as it directly influences properties suc…
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…
Four-Dimensional-Spacetime Atomistic Artificial Intelligence Models
Fuchun Ge, Lina Zhang, Yi-Fan Hou +3
We demonstrate that AI can learn atomistic systems in the four-dimensional (4D) spacetime. For this, we introduce the 4D-spacetime GICnet model which for the given initial conditio…
Energy-conserving molecular dynamics is not energy conserving
Lina Zhang, Yi-Fan Hou, Fuchun Ge +1
Molecular dynamics (MD) is a widely-used tool for simulating the molecular and materials properties. It is a common wisdom that molecular dynamics simulations should obey physical…