32nd CCS Congress Poster Download Link
Download the poster by Bao-Xin Xue about Machine Learning for Absorption Cross Sections:
Category: Machine Learning in Chemistry
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 on ChemRxiv, DOI: 10.26434/chemrxiv.12594191. Short overview of the method in a form of LiveSlides: In brief ML-NEA can boost the calculation speed and increase …
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.
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.
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 …
Self-Correcting Machine Learning and Structure-Based Sampling Read more »