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!
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 …
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: https://www.nature.com/collections/gagdjjgcgj AIQM1 paper: https://www.nature.com/articles/s41467-021-27340-2 How to use AIQM1 method with MLatom: http://MLatom.com/AIQM1
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。
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 …