Materials design with MLatom for ammonia separation and storage
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.
Author: Pavlo Dral
Congratulations to Bao-Xin Xue with the defense of his Master thesis!
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!
Plenary lecture at ANSCSE25
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 …
MLatom joins Xiamen Atomistic Computing Suite
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 …
AIQM1 paper is top 25 most downloaded Nature Communications articles in chemistry and materials sciences published in 2021
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
Open post-doc position
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。
Artificial intelligence makes accurate quantum chemical simulations more affordable
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 »
SMLQC-2021 is over. Viva SMLQC!
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 …
MLatom 2: Introducing a Platform for Atomistic Machine Learning
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 »
Review on Machine Learning for Molecular Excited States
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.