The very first SMLQC seminar will be given by Arif Ullah on Feb. 2, 2023 (15:00 Paris | 22:00 Beijing | 09:00 New York).
Title
Quantum Dissipative Dynamics with Machine Learning
Abstract
In this talk, I will present our work on Machine Learning-based Quantum Dissipative Dynamics methods such as AIQD[1] and OSTL[2]. I will also talk about our recently released MLQD package (GitHub) and QD3SET-1 (GitHub) database. The talk will be followed by a tutorial demonstrating the applicability of our methods on our MLatom@XACS cloud computing platform.
- For standalone and up-to-date version of MLQD package, visit GitHub repository https://github.com/Arif-PhyChem/MLQD
- Link to manual: http://mlatom.com/manual/#mlqd
- Link to detailed-tutorial on using MLQD on XACS cloud computing plateform: http://mlatom.com/tutorial/mlqd/
References
- Arif Ullah*, Pavlo O. Dral*, Predicting the future of excitation energy transfer in light-harvesting complex with artificial intelligence-based quantum dynamics, Nat. Commun. 2022, 13, 1930. DOI: 10.1038/s41467-022-29621-w. (blog post)
- Arif Ullah*, Pavlo O. Dral*, One-shot trajectory learning of open quantum systems dynamics, J. Phys. Chem. Lett. 2022, 13, 6037–6041. DOI: https://doi.org/10.1021/acs.jpclett.2c01242 | (blog post)