It is time for drawing up conclusions for the year and here is my brief overview of our group’s third year. 2022 will come down in history as a tumultuous year with major global events which couldn’t not affect me …
The group of Prof. Pavlo Dral in College of Chemistry and Chemical Engineering at Xiamen University has Master’s positions starting from fall 2023. Prof. Dral has a decade-long successful expertise in artificial intelligence for transforming computational chemistry, and authored ca. …
We are happy to announce that our brand-new manual for MLatom@XACS is now online at http://mlatom.com/manual/. Our new manual is re-designed to make it much easier to find what you need. We also greatly updated it with additional information, useful tips, …
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.
The man behind machine learning-nuclear ensemble approach graduated! Congratulations to Baoxin, my very first graduate student! We wish him best of luck in his future endeavors!