70 citations · 169 across the 7 of their papers we have counts for
8 papers
Can Machine Learning Potentials Be More Accurate Than the Methods They Are Trained On?
Pavlo O. Dral, Yi-Fan Hou, Yuxinxin Chen +4
Although machine learning potentials (MLPs) generalize across configurational and chemical compound spaces, their accuracy is commonly believed to be limited by their training meth…
A Descriptor Is All You Need: Accurate Machine Learning of Nonadiabatic Coupling Vectors
Jakub Martinka, Lina Zhang, Yi-Fan Hou +4
Nonadiabatic couplings (NACs) play a crucial role in modeling photochemical and photophysical processes with methods such as the widely used fewest-switches surface hopping (FSSH).…
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…
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…
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…
Localization of gravitational waves using machine learning
Seiya Sasaoka, Hirotaka Takahashi, Yilun Hou +1
An observation of gravitational waves is a trigger of the multi-messenger search of an astronomical event. A combination of the data from two or three gravitational wave telescopes…