In the work published in New Journal of Physics, we combine machine learning (ML) with the numerically exact hierarchical equations of motion (HEOM) approach, propagating quantum dynamics of a two-state system (spin-boson model) with only ca. 10% of the HEOM …
Speeding up quantum dissipative dynamics of open systems with kernel methods Read more »
We report global potential energy surfaces (PESs) database VIB5 of 5 molecules of astrophysical interest which was used to produce rovibrational spectra approaching spectroscopic accuracy and contains state-of-the-art, high-level energies and energy gradients. This database can be used to develop …
We have developed artificial intelligence-enhanced quantum mechanical method 1 (AIQM1), which can be used out of the box for very fast quantum chemical calculations with the accuracy of the gold-standard coupled-cluster method. Read more »
Symposium on Machine Learning in Quantum Chemistry 2021 (SMLQC-2021) has been a huge success with many great talks and discussions, chats after talks, and poster sessions! This success has prompted us to continue and expand this kind of events on request …
Lost in the sea of all machine learning potentials? Our overview and recommendations based on balanced analysis are just out in Chemical Science. In brief, kernel methods are a better choice for not too large data in terms of both …
Fuchun Ge, Ran Wang, and Linqiang Wei join our group starting this month. Welcome! Fuchun officially joins our group as a PhD student. He has just finished his master in analytical chemistry. Before that he already made significant contribution to …
The group of Dr. Pavlo Dral in College of Chemistry and Chemical Engineering at Xiamen University has PhD and Master positions starting from fall 2022. Our group focuses on the application of machine learning in quantum chemistry and projects focusing …
We are happy to introduce MLatom 2: a major release of our integrative platform for user-friendly atomistic machine learning. It includes many more features and is further optimized for efficiency. Detailed overview of MLatom 2 is given in our contribution …
MLatom 2: Introducing a Platform for Atomistic Machine Learning Read more »
In our Review “Molecular excited states through a machine learning lens” in Nature Reviews Chemistry, we provide insights and highlight challenges of the rapidly growing field of machine learning for excited-states simulation and analysis.
Download the poster by Bao-Xin Xue about Machine Learning for Absorption Cross Sections: