报告人：Sergei Tretiak

报告题目：In the quest for excited states, from machine learning to non-adiabatic dynamics

报告摘要：Machine learning (ML) is quickly becoming a premier tool for modeling chemical processes and materials. Designing high-quality training data sets is crucial to overall model accuracy. I will describe the active learning strategy, in which new data are automatically collected for atomic configurations that produce large ML uncertainties. The locality approximation underpinning favorable computational scaling of the ML models, is another severe limitation that fails to capture long-range effects. I will discuss how ML models can overcome nonlocality (via introduction of interaction layers, self-consistent cycles, or charge equilibration schemes) and exemplify their performance for chemical problems. All these advances are exemplified by applications to molecules and materials. Exciting new method development and explosive growth of user-friendly ML frameworks, designed for chemistry, demonstrate that the field is evolving towards physics-based models augmented by data science. I will further overview some applications of Non-adiabatic EXcited-state Molecular Dynamics (NEXMD) framework developed at several institutions. The NEXMD code is able to simulate tens of picoseconds photoinduced dynamics in large molecular systems. As an application, I will exemplify ultrafast coherent excitonic dynamics guided by intermolecular conical intersections (CoIns). Both simulations and time-resolved 2D electronic spectroscopy track the coherent motion of a vibronic wave packet, a process that governs the ultrafast energy transfer dynamics in molecular aggregates. Our results suggest that intermolecular CoIns may effectively steer energy pathways in functional nanostructures. In the second example, we use NEXMD simulations to compute X-ray Raman signals, which are able to sensitively monitor the coherence evolution. The observed coherences have vibronic nature that survives multiple conical intersection passages for several hundred femtoseconds at room temperature. These spectroscopic signals are possible to measure at XFEL facilities. Our modeling results allow us to understand and potentially manipulate excited state dynamics and energy transfer pathways toward optoelectronic applications.

报告简介：Sergei Tretiak is a T-1 deputy group Leader in the Theoretical Division at Los Alamos National Laboratory (LANL) and a Los Alamos National Laboratory Fellow. He received his Master’s degree in Physics in 1994 from Moscow Institute of Physics and Technology (Russia) and his Chemistry doctorate in 1998 from the University of Rochester (US). He was then a Director-funded postdoctoral fellow (1999-2001), and subsequently became a staff scientist at LANL and a member of the DOE-funded Center for Integrated Nanotechnologies (CINT). Tretiak also serves as Adjunct Professor at the University of California, Santa Barbara (UCSB) (2015-present). He became an American Physical Society Fellow (APS) in 2014 and a Fellow of the Royal Society of Chemistry, (RSC) in 2019. He has also received the Humboldt Research Award (2021), the Los Alamos Postdoctoral Distinguished Mentor Award (2015) and the Los Alamos Fellow's Prize for Research (2010). His research interests include development of electronic structure methods for molecular optical properties, nonlinear optical response of organic chromophores, non-adiabatic dynamics of electronically excited states, optical response of confined excitons in conjugated polymers, carbon nanotubes, semiconductor nanoparticles, mixed halide perovskites and molecular aggregates, the use of Machine Learning and Data Science toward modeling electronic and chemical properties. Tretiak has published nearly 400 scientific publications cited more than 22,000 times (h-index=71, WebOfSci) and he has presented more than 300 invited and keynote talks in the US and abroad.