XACS team in collaboration with Mario Barbatti and groups in Warsaw University and Zhejiang lab has recently published a paper in JCTC about the versatile Python implementation of surface-hopping dynamics. This implementation is based on a powerful MLatom ecosystem for …
JCTC: Surface hopping dynamics with QM and ML methods Read more »
A machine learning potential with low error in the potential energies does not guarantee good performance for the simulations. One of the reasons is that it is hard to train machine learning potentials with balanced descriptions of different PES regions, …
My review ‘AI in computational chemistry through the lens of a decade-long journey’ was published open access as an invited Feature Article in Chemical Communication. It gives a perspective on the progress of AI tools in computational chemistry through the …
AI-accelerated nonadiabatic dynamics reduces the cost of the ab initio simulations of nonlinear time-resolved spectra. We have developed a robust protocol and demonstrated its feasibility for calculating stimulated emission contributions in transient absorption pump–probe and 2D electronic spectra of pyrazine. …
Surging efforts and fast progress in AI methods for photochemistry and photophysics make it difficult to track the current state of the art. We cover the recent developments in this field in the chapter on Machine learning methods in photochemistry …
Chapter “Machine Learning Methods in Photochemistry and Photophysics” Read more »
Check out the themed collection and our editorial on Insightful machine learning for physical chemistry in PCCP – it was a pleasure for me to guest edit it with Aurora Clark, Olexandr Isayev, and Isaac Tamblyn. Happy reading!
Activation of methane and its conversion to added-value products is an important topic which requires chemical solutions with high yields and selectivity. In our recent collaborative study, we present the oxidation of methane to methanol using nitrogen dioxide as a …
We improved (p)KREG models for an accurate representation of molecular potential energy surfaces (PESs) by including gradient information explicitly in their formalism. Our models are better or on par with other state-of-the-art machine learning models as we show on extensive …
(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 via click chemistry. We used our state-of-the-art, artificial intelligence-enhanced quantum mechanical method 1 (AIQM1) to thoroughly analyze the electronic and photophysical properties of these …
Large Cycloparaphenylene Nanolassos Characterized with AIQM1 Read more »
Materials can simultaneously absorb not just one but two photons and molecules with strong two-photon absorption (TPA) are important in many fields such as unconverted laser, photodynamic therapy, and 3D printing. In our work published in Advanced Science (open access), …
Explaining and Predicting Two-Photon Absorption with Machine Learning Read more »