Machine Learning and Quantum Computing for Quantum Molecular Dynamics [MLQCDyn]

Published Time:  2022-09-21 09:46:35

MLatom@XACS team introduced how to use machine learning in chemistry in the CECAM Machine Learning and Quantum Computing for Quantum Molecular Dynamics [MLQCDyn] school. This school aimed at offering state-of-the-art training in quantum molecular dynamics (QMD), machine learning (ML), and quantum computing (QC) to early-stage scientists, including PhD and postdoctoral researchers coming mainly from the molecular dynamics community. The school was part of a Thematic Program of the Pascal Institute of the University Paris-Saclay spanning a total of 4 weeks covering the same topics as a school. A founder of MLatom, Pavlo O. Dral (Xiamen University), was a co-organizer of CECAM school together with Mario Barbatti (Aix Marseille University), Majdi Hochlaf (Université Gustave Eiffel), Artur Izmaylov (University of Toronto), Dario Rocca (QC Ware Corp.), and Julia Westermayr (University of Warwick). The event was hybrid.

Our MLatom@XACS team participated in giving two lectures and two practical sessions with hands-on tutorials which you can also follow yourself as we published all slides and tutorials! Here are the links:
Lecture 3: General introduction to ML by Pavlo O. Dral (online).
Practical session 3: First steps into ML by Pavlo O. Dral (online) and Max Pinheiro Jr. (on site). (tutorial)
Part 2 of Lecture 5: ML molecular dynamics: ground state by Pavlo O. Dral (online). (slides)
Practical session 6: Analysing dynamics with unsupervised ML by Max Pinheiro Jr (on site). (tutorial)

Finally, MLatom@XACS team presented several posters showcasing some of our latest developments:
Poster 12: Atomistic machine learning made easy with MLatom@XACS by Fuchun Ge
Poster 13: Molecular dynamics and infrared spectra simulations with MLatom@XACS by Yi-Fan Hou
Poster 11: VIB5 database with accurate ab initio quantum chemical molecular potential energy surfaces by Lina Zhang