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 →
In a recent article published in Frontiers in Physics, we introduce QD3SET-1, a database consisting of …
QD3SET-1: A Database with Quantum Dissipative Dynamics Data Sets Read more →
The second edition of the International Symposium on Machine Learning in Quantum Chemistry will be held …
SMLQC-2023: The 2nd International Symposium on Machine Learning in Quantum Chemistry Read more →
We improved (p)KREG models for an accurate representation of molecular potential energy surfaces (PESs) by including …
(p)KREG Models for Accurate Molecular Potential Energy Surfaces Read more →
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 →
In our work published in the Journal of Chemical Physics, we investigate the performance of AIQM1 on …
Materials can simultaneously absorb not just one but two photons and molecules with strong two-photon absorption …
Explaining and Predicting Two-Photon Absorption with Machine Learning Read more →
We are happy to announce that our brand-new manual for MLatom@XACS is now online at http://mlatom.com/manual/. Our …
Recently, Machine Learning (ML) is increasingly used for fast and accurate propagation of quantum dissipative dynamics …
Mario Barbatti, his group and collaborators published an update on Newton-X – a popular open-source platform…