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 …
Lost in the sea of all machine learning potentials? Our overview and recommendations based on balanced analysis are just out in Chemical Science. In brief, kernel methods are a better choice for not too large data in terms of both …
Fuchun Ge, Ran Wang, and Linqiang Wei join our group starting this month. Welcome! Fuchun officially joins our group as a PhD student. He has just finished his master in analytical chemistry. Before that he already made significant contribution to …
The group of Dr. Pavlo Dral in College of Chemistry and Chemical Engineering at Xiamen University has PhD and Master positions starting from fall 2022. Our group focuses on the application of machine learning in quantum chemistry and projects focusing …
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.
Download the poster by Bao-Xin Xue about Machine Learning for Absorption Cross Sections:
Paper Bao-Xin Xue, Mario Barbatti*, Pavlo O. Dral*, Machine Learning for Absorption Cross Sections, J. Phys. Chem. A 2020, 124, 7199–7210. DOI: 10.1021/acs.jpca.0c05310.Preprint on ChemRxiv, DOI: 10.26434/chemrxiv.12594191. Short overview of the method in a form of LiveSlides: In brief ML-NEA can boost the calculation speed and increase …