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 chemistry research.
Tag: ML
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.
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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Paper on MLatom
The mathematical and implementation details of the techniques available in MLatom: A Package for Atomistic Simulations with Machine Learning are reported.
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 of molecular systems.
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 and calculating vibrational levels with spectroscopic accuracy (errors less than 1 cm−1 relative to the reference ab initio spectrum) decreasing the number of required …
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Highlight about Δ-ML Approach Most Viewed in 2015
A highlight by Jan Jensen about the Δ-ML approach proposed by us [1] was the most viewed highlight in Computational Chemistry Highlights in 2015. This marks a pleasant ending of the last-year research on improving accuracy of computationally less demanding …
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