15 citations · 15 across the 1 of their papers we have counts for
5 papers · 1 filter
All-in-one foundational models learning across quantum chemical levels
Yuxinxin Chen, Pavlo O. Dral
Machine learning (ML) potentials typically target a single quantum chemical (QC) level while the ML models developed for multi-fidelity learning have not been shown to provide scal…
Molecular Quantum Chemical Data Sets and Databases for Machine Learning Potentials
Arif Ullah, Yuxinxin Chen, Pavlo O. Dral
The field of computational chemistry is increasingly leveraging machine learning (ML) potentials to predict molecular properties with high accuracy and efficiency, providing a viab…
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…
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…