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 …
Our recent article in npj Computational Materials presents an efficient ML protocol for accelerating trajectory surface …
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 →
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 →
I have presented on March 20, 2024, the ongoing journey towards making excited-state simulations more accessible …
VISTA: Towards more accessible excited-state simulations with AI Read more →
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 →
AI-accelerated nonadiabatic dynamics reduces the cost of the ab initio simulations of nonlinear time-resolved spectra. We …
Surging efforts and fast progress in AI methods for photochemistry and photophysics make it difficult to …
Chapter “Machine Learning Methods in Photochemistry and Photophysics” Read more →
Activation of methane and its conversion to added-value products is an important topic which requires chemical …
Alkyne-embedding [11]cycloparaphenylene ([11]CPPs) was functionalized with electron-donating, -neutral, and -withdrawing aryl substituents to yield a series of nanolassos …
Large Cycloparaphenylene Nanolassos Characterized with AIQM1 Read more →
Mario Barbatti, his group and collaborators published an update on Newton-X – a popular open-source platform…