Recently, we published a paper in JCTC about the end-to-end physics-informed active learning with data-efficient construction of machine learning potentials. It shortens molecular simulation time to a couple of days which could have taken weeks of pure quantum chemical calculations.

The work “Physics-Informed Neural Networks and Beyond: Enforcing Physical Constraints in Quantum Dissipative Dynamics” performed in collaboration of Professor Pavlo O. Dral (Xiamen University) and Assistant Professor Arif Ullah (Anhui University) was published in Digital Discovery. In this blog, Arif Ullah highlights this …

Physically-consistent quantum dissipative dynamics simulations with neural networks Read more »

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, …

JPCL | Tell Machine Learning Potentials What They Are Needed For: Simulation-Oriented Training Read more »

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 …

Chem. Commun. Feature Article: “AI in computational chemistry through the lens of a decade-long journey” Read more »

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. …

Artificial-Intelligence-Enhanced On-the-Fly Simulation of Nonlinear Time-Resolved Spectra Read more »

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 »

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 …

Methane Conversion via Photo-Driven Nitration Read more »

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 »