Search Results for « SchNet »

Quantum materials present significant challenges due to their inherent complexity in physical systems. With the rapid development of deep learning, it is accessible to make prediction of the complicated behaviors in quantum materials. This paper proposed a new neural network SchNet+ to better narrate the complexity of systems, which can not be easily solved by traditional mathematical equations.

Abstract In recent years, deep learning has become a part of our everyday life and is revolutionizing quantum chemistry as well. In this work, we show how deep learning can be used to advance the research field of photochemistry by learning all important properties─multiple energies, forces, and different couplings─for photodynamics simulations. We simplify such simulations substantially by (i) a phase-free training skipping costly preprocessing of raw quantum chemistry data; (ii) rotationally covariant nonadiabatic couplings, which can either be trained or (iii) alternatively be approximated from only ML potentials, their gradients, and Hessians; and (iv) incorporating spin–orbit couplings. As the deep-learning method, we employ SchNet with its automatically determined representation of molecular structures and extend it for multiple electronic states. In combination with the molecular dynamics program SHARC, our approach termed SchNarc is tested on two polyatomic molecules and paves the way toward efficient photodynamics simulations of complex systems.