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Found 3 papers, 3 papers with code
A Descriptor Is All You Need: Accurate Machine Learning of Nonadiabatic Coupling Vectors
We demonstrate the efficiency and robustness of our approach on a prototypical example of fully ML-driven FSSH simulations of fulvene targeting the SA-2-CASSCF(6, 6) electronic structure level.
Physics-informed active learning for accelerating quantum chemical simulations
Quantum chemical simulations can be greatly accelerated by constructing machine learning potentials, which is often done using active learning (AL).
MLatom 3: Platform for machine learning-enhanced computational chemistry simulations and workflows
MLatom 3 is a program package designed to leverage the power of ML to enhance typical computational chemistry simulations and to create complex workflows.
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