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 »

Our AIQM1 paper is one of the 25 most downloaded Nature Communications articles in chemistry and materials sciences published in 2021! #NCOMTop25 The full list: AIQM1 paper: How to use AIQM1 method with MLatom:

The group of Associate Professor Pavlo O. Dral is looking for a post-doc with a proven track record of the development, implementation, and application of theoretical chemistry methods for solid-state simulations. Pavlo O. Dral副教授课题组,拟招聘博士后一名,欢迎具有固态模拟理论化学方法的开发、实践和应用方面研究经历并有志于科学研究的青年才俊加盟。