npj Comput. Mater.: Efficient Machine Learning Protocol For Accelerating Trajectory Surface Hopping Dynamics
Our recent article in npj Computational Materials presents an efficient ML protocol for accelerating trajectory surface …
Tag: nonadiabatic dynamics
Meet OMNI-P2x — the First Universal ML Potential for Excited States!
Are universal machine learning potentials for excited states possible? Such a potential would be a major …
Meet OMNI-P2x — the First Universal ML Potential for Excited States! Read more →
JCTC: Surface hopping dynamics with QM and ML methods
XACS team in collaboration with Mario Barbatti and groups in Warsaw University and Zhejiang lab has …
JCTC: Surface hopping dynamics with QM and ML methods Read more →
Surface hopping dynamics with MLatom is coming: Join online broadcast!
MLatom@XACS makes AI-enhanced computational chemistry more accessible and supports both ground- and excited-state simulations with quantum …
Surface hopping dynamics with MLatom is coming: Join online broadcast! Read more →
The Newton-X platform for surface hopping and nuclear ensembles
Mario Barbatti, his group and collaborators published an update on Newton-X – a popular open-source platform…
Chapter on Machine Learning in Quantum Chemistry in a Tutorial Way
My book chapter shows in a tutorial way how to use machine learning to assist quantum …
Chapter on Machine Learning in Quantum Chemistry in a Tutorial Way Read more →
Nonadiabatic Dynamics with Deep Learning
We demonstrate that deep learning can be used to perform pure machine learning nonadiabatic excited-state dynamics …
Machine Learning Accelerates Excited-State Dynamics
Machine learning paves the way for massive simulations of nonadiabatic excited-state molecular dynamics.