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.
My perspective on the state-of-the-art of machine learning in quantum chemistry and outlook for future developments was published in J. Phys. Chem. Lett.
New alternative to “magic blue” — a standard oxidant in organic chemistry — has been prepared and its properties were rationalized computationally.
A post-doctoral position is open in the group of Dr. Pavlo Dral in College of Chemistry and Chemical Engineering at Xiamen University for the development of cutting-edge machine learning and quantum chemistry methods. The earliest starting date is January 2020. …
I am happy to announce that I am joining Xiamen University as an Associate Professor.
Prof. Walter Thiel passed away unexpectedly on August 23, 2019.
The mathematical and implementation details of the techniques available in MLatom: A Package for Atomistic Simulations with Machine Learning are reported.
Johannes Margraf and I have published our perspective on what semiempirical molecular orbital (SEMO) methods are and should be approximating in the article dedicated to the 70th birthday of our PhD supervisor Tim Clark.