Perspective on Machine Learning in Quantum Chemistry
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.
Tag: J. Phys. Chem. Lett.
Nonadiabatic Dynamics with Deep Learning
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
Machine learning paves the way for massive simulations of nonadiabatic excited-state molecular dynamics.