Yuxinxin Chen (master student), Yeyun Chen (co-supervised PhD student) and Yaohuang Huang (research assistant) join our group this semester. Welcome! Yuxinxin Chen continues her education in our group as a master student and explores the application of AIQM1 in reaction …
Recently, Machine Learning (ML) is increasingly used for fast and accurate propagation of quantum dissipative dynamics including our works for the two-state system and seven-site FMO complex. The studies carried out so far demonstrated the use of different ML models …
Mario Barbatti, his group and collaborators published an update on Newton-X – a popular open-source platform for surface hopping and nuclear ensembles. An update include extension of the Newton-X platform to supervised (with our MLatom platform) and unsupervised learning (with ulamdyn). The paper is also open access and appeared in the Journal of Chemical Theory and Computation.
The book “Quantum Chemistry in the Age of Machine Learning” guides aspiring beginners and specialists in this exciting field by covering topics ranging from basic concepts to comprehensive methodological details in machine learning, quantum chemistry, and their combinations in a …
Book “Quantum Chemistry in the Age of Machine Learning” Read more »
MLatom@XACS team introduced how to use machine learning in chemistry in the CECAM Machine Learning and Quantum Computing for Quantum Molecular Dynamics [MLQCDyn] school. This school aimed at offering state-of-the-art training in quantum molecular dynamics (QMD), machine learning (ML), and quantum computing (QC) to early-stage …
Tutorial on ML in CECAM school MLQCDyn featuring MLatom@XACS Read more »
We used our MLatom package to develop a machine learning approach for designing materials based on mixed metal halides to facilitate ammonia separation and storage.
In the work published in the Journal of Physical Chemistry Letters, we have proposed a one-shot trajectory learning (OSTL) approach that allows an ultrafast prediction of 10-ps-long quantum dynamics of an open quantum system just in 70 milliseconds. OSTL approach takes …
One-Shot Trajectory Learning of Open Quantum Systems Dynamics Read more »
It has been a great pleasure to give a plenary lecture at the 25th International Annual Symposium on Computational Science and Engineering (ANSCSE25). I was talking about AI/ML in computational chemistry, how our MLatom can help with it, and introduced …
We are very happy to announce that MLatom joins Xiamen Atomistic Computing Suite (XACS) which allows us to provide much better service to the theoretical and computational chemistry community. We marked the inception of XACS by holding a local workshop …
In our work published in the Journal of Physical Chemistry Letters, we investigate the performance of the general-purpose data-driven methods ANI-1ccx and AIQM1 in the calculation of enthalpies of formation. Extensive benchmark tests show that these two methods can achieve …