Author: Pavlo Dral

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. …

Post-doctoral Position Opening in Machine Learning in Quantum Chemistry Read More »

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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.

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The mathematical and implementation details of the techniques available in MLatom: A Package for Atomistic Simulations with Machine Learning are reported.

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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.

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MLatom 1.0 release of my package for atomistic simulations with machine learning is now available.

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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.

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We comprehensively analyzed the validity of the NDDO (neglect of diatomic differential overlap) approximation, which forms the basis for most modern semiempirical quantum chemical methods.

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We demonstrate that deep learning can be used to perform pure machine learning nonadiabatic excited-state dynamics of molecular systems.

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Machine learning paves the way for massive simulations of nonadiabatic excited-state molecular dynamics.

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