70 citations · 158 across the 7 of their papers we have counts for
7 papers
Artificial Intelligence for Direct Prediction of Molecular Dynamics Across Chemical Space
Fuchun Ge, Yuxinxin Chen, Pavlo O. Dral
Molecular dynamics (MD) is a powerful tool for exploring the behavior of atomistic systems, but its reliance on sequential numerical integration limits simulation efficiency. We pr…
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…
MLatom 3: Platform for machine learning-enhanced computational chemistry simulations and workflows
Pavlo O. Dral, Fuchun Ge, Yi-Fan Hou +15
Machine learning (ML) is increasingly becoming a common tool in computational chemistry. At the same time, the rapid development of ML methods requires a flexible software framewor…
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…