Theoretical IR (infrared) spectroscopy is a powerful tool for assisting chemical structure identification. However, approaches based on …
ML-enhanced Fast and Interpretable Simulation of IR Spectra Read more →
Density functional theory (DFT) methods are by far the most popular approaches for electronic structure calculations. …
Adv. Sci.: The Best DFT Functional Is the Ensemble of Functionals Read more →
Recently, we published a paper in JOC about the surprising dynamics phenomena in the Diels–Alder reaction …
JOC: Surprising dynamics phenomena in the Diels–Alder reaction of C60 uncovered with AI Read more →
Recently, we published a paper in JCTC about the end-to-end physics-informed active learning with data-efficient construction of machine …
JCTC: Physics-informed active learning for accelerating quantum chemical simulations Read more →
The work “Physics-Informed Neural Networks and Beyond: Enforcing Physical Constraints in Quantum Dissipative Dynamics” performed in …
Physically-consistent quantum dissipative dynamics simulations with neural networks Read more →
A year ago, we released MLatom 3, making MLatom the fully-fledged Python package. This shift endowed …
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 →
Institution: Department of Chemistry, Xiamen University, China PI: Prof. Dr. Pavlo O. Dral (http://dr-dral.com) Duration: 1–3 …
Postdoc in method development for materials or molecular simulations Read more →
A machine learning potential with low error in the potential energies does not guarantee good performance …
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 →