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 …
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 »
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.
Diazapentacene derivatives were synthesized and investigated for their potential application in organic field-effect transistors, with one derivative showing a rare n-type behavior. The blog post written by Wudi Yang and Shuang Zhang and edited by Pavlo Dral. Organic field-effect transistors …
My book chapter shows in a tutorial way how to use machine learning to assist quantum chemistry research.
Theory was instrumental in rationalizing complex photophysical phenomena experimentally observed for a series of spiro-bridged heterotriangulenes in solution and their aggregates.
We introduced hierarchical machine learning (hML) approach for building highly accurate potential energy surfaces from multiple Δ-ML models, each trained on semi-automatically defined training points.