hML: Hierarchical Machine Learning for PESs
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.
Perspective on Machine Learning in Quantum Chemistry
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.
Stable Triphenylamine Radical Cation
New alternative to “magic blue” — a standard oxidant in organic chemistry — has been prepared and its properties were rationalized computationally.
Post-doctoral Position Opening in Machine Learning in Quantum Chemistry
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. …
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Joining Xiamen University
I am happy to announce that I am joining Xiamen University as an Associate Professor.
Walter Thiel
Prof. Walter Thiel passed away unexpectedly on August 23, 2019.
Paper on MLatom
The mathematical and implementation details of the techniques available in MLatom: A Package for Atomistic Simulations with Machine Learning are reported.
Next Step: TB-SEMO Methods?
Johannes Margraf and I have published our perspective on what semiempirical molecular orbital (SEMO) methods are and should be approximating in the article[1] dedicated to the 70th birthday of our PhD supervisor Tim Clark.
MLatom 1.0
MLatom 1.0 release of my package for atomistic simulations with machine learning is now available.
ODMx: New Consistent Semiempirical Methods
We have introduced two new NDDO-based semiempirical quantum-chemical methods ODM2 and ODM3, which are more consistent and accurate than other existing methods of this type.