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Home › Posts tagged J. Phys. Chem. Lett.

Tag: J. Phys. Chem. Lett.

Perspective on Machine Learning in Quantum Chemistry

By Pavlo Dral Posted on May 28, 2020 Posted in Machine Learning in Chemistry No Comments Tagged with , J. Phys. Chem. Lett.MLMLatom

My perspective on the state-of-the-art of machine learning in quantum chemistry and outlook for future developments was published in J. Phys. Chem. Lett.

Nonadiabatic Dynamics with Deep Learning

By Pavlo Dral Posted on November 15, 2018 Posted in Machine Learning in Chemistry No Comments Tagged with , excited statesJ. Phys. Chem. Lett.MLnonadiabatic dynamics

We demonstrate that deep learning can be used to perform pure machine learning nonadiabatic excited-state dynamics of molecular systems.

Machine Learning Accelerates Excited-State Dynamics

By Pavlo Dral Posted on September 14, 2018 Posted in Machine Learning in Chemistry, News 2 Comments Tagged with , excited statesJ. Phys. Chem. Lett.MLnonadiabatic dynamics

Machine learning paves the way for massive simulations of nonadiabatic excited-state molecular dynamics.

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