70 citations · 137 across the 5 of their papers we have counts for
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physics.chem-ph2024★ 4 cited
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…
physics.chem-ph2024★ 17 cited
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…
physics.chem-ph2024★ 31 cited
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…