Associate Prof. Pavlo O. Dral and postdoc scholar Arif Ullah of our school have developed a blazingly fast artificial intelligence (AI)-based quantum dynamics (QD) approach published in Nature Communications with the title “Predicting the future of excitation energy transfer in light-harvesting complex with artificial intelligence-based quantum dynamics”.

With the AI-QD approach, the authors propose a new way to propagate quantum dynamics circumventing the need of traditional iterative dynamics. Just by providing parameters such as reorganization energy λ, characteristic frequency γ, temperature T etc., the proposed AI-QD approach can predict the corresponding trajectory up to its asymptotic limit. As the proposed approach is non-iterative, it means that all time-steps are independent from each other, hence allows one to perform calculations in parallel, as a result enormously speeding up the calculations. As an application, the authors have explored the highly efficient excitation energy transfer in the well-known Fenna-Matthews-Olsen (FMO) complex found in green sulfur bacteria which is of great interest in the so-called biomimetic light-harvesting engineering focused on designing highly efficient organic solar devices.

The work has been supported by the National Natural Science Foundation of China (No. 22003051), the Fundamental Research Funds for the Central Universities (No. 20720210092).

Link: https://doi.org/10.1038/s41467-022-29621-w