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 …
Tag: ML
Chapter on Machine Learning in Quantum Chemistry in a Tutorial Way
My book chapter shows in a tutorial way how to use machine learning to assist quantum …
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hML: Hierarchical Machine Learning for PESs
We introduced hierarchical machine learning (hML) approach for building highly accurate potential energy surfaces from multiple …
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 …
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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 …
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Paper on MLatom
The mathematical and implementation details of the techniques available in MLatom: A Package for Atomistic Simulations …
MLatom 1.0
MLatom 1.0 release of my package for atomistic simulations with machine learning is now available.
Nonadiabatic Dynamics with Deep Learning
We demonstrate that deep learning can be used to perform pure machine learning nonadiabatic excited-state dynamics …
Machine Learning Accelerates Excited-State Dynamics
Machine learning paves the way for massive simulations of nonadiabatic excited-state molecular dynamics.
Self-Correcting Machine Learning and Structure-Based Sampling
Structure-based sampling and self-correcting machine learning is used for precise representation of molecular potential energy surfaces …
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