Abstract Precision spectroscopy of the hydrogen atom, a fundamental two-body system, has been instrumental in shaping quantum mechanics. Today, advances in theory and experiment allow us to extend such high-precision studies to more complex many-body systems. In this talk, the speaker will present his team's recent laser spectroscopy work on a select class of computable systems — including atomic helium (three-body), molecular hydrogen (four-body), and multi-electron species like CO — where state-of-the-art ab initio calculations achieve spectroscopic accuracy. By comparing experimental and theoretical transition frequencies at the 12-digit level, the team is able to test quantum mechanics and reveal novel quantum phenomena. Beyond frequencies, the team's ppm-level measurements of spectral intensities open applications in primary thermometry, isotopic analysis, and gas metrology. However, persistent interlaboratory discrepancies highlight remaining challenges, motivating further refinement of both experimental and theoretical approaches. About the Speaker Prof. HU Shui-Ming received his BS (1995) and PhD (2000) in Chemical Physics from the University of Science and Technology of China (USTC), followed by postdoctoral research at Rice University and Argonne National Laboratory. Awarded the prestigious National Science Fund for Distinguished Young Scholars in 2012, he currently serves as Professor of Chemical Physics at USTC, the Director of the Division of Advanced Measurement Instruments at the Hefei National Research Center for Physical Sciences at the Microscale, and Council Member of the Chinese Society for Measurement. His research focuses on developing novel laser spectroscopy techniques with ultrahigh precision and sensitivity, with applications spanning fundamental studies of atomic and molecular physics, chemical kinetics, optical metrology, trace gas analysis, and the development of advanced spectroscopic instrumentation for both scientific and industrial applications.  For Attendees' Attention Seating is on a first come, first served basis.

Abstract Surfaces and interfaces are ubiquitous in Nature. Sum-frequency generation vibrational spectroscopy (SFG-VS) is a powerful surface/interface selective and sub-monolayer sensitive spectroscopic technique to interrogate the vibrational spectroscopy, structure, and conformation, as well as optical activity, and vibrational dynamics of molecular surfaces and interfaces. The difficulties that have limited the application of SFG-VS to broad scientific problems regarding complex surfaces and interfaces, such as the difficulties in spectral assignment, accurate measurement and experimental as well as computational analysis of the SFG spectrum, have been mostly overcome with the recent developments. This presentation discusses the most recent developments in SFG-VS, particularly on the further development of the quality and sensitivity of the sub-wavenumber high resolution broadband SFG-VS (HR-BB-SFG-VS), and the future perspectives of the applications of SFG-VS as well as other nonlinear vibrational spectroscopy to surface/interface studies and beyond.1,2 References: [1] Wang, H. F.; Velarde, L.; Gan, W.; Fu, L., Quantitative sum-frequency generation vibrational spectroscopy of molecular surfaces and interfaces: Lineshape, polarization, and orientation. 2015, Annu. Rev. Phys. Chem., 66, 189-216. [2] Wang, H.-F., Sum frequency generation vibrational spectroscopy (SFG-VS) for complex molecular surfaces and interfaces: Spectral lineshape measurement and analysis plus some controversial issues. 2016, Prog. Surf. Sci., 91, 155-182. About the Speaker Prof. WANG Hongfei received his BS in Chemical Physics from the University of Science and Technology of China (USTC) in 1988 and continued his graduate studies in laser chemistry at USTC until 1991. He then obtained his PhD in Physical Chemistry from Columbia University in 1996. After two and a half years of postdoctoral work at the University of Pennsylvania, in collaboration with the DuPont Marshall Laboratory in Philadelphia, he joined the Molecular Reaction Dynamics Laboratory at the Institute of Chemistry, Chinese Academy of Sciences (ICCAS) as a Professor. At ICCAS, he served as the Director of the Molecular Reaction Dynamics Laboratory from 2000 to 2004 and as the Deputy Director of the State Key Laboratory of Molecular Reaction Dynamics during the same period. In 2009, he moved to the US and joined the Environmental Molecular Sciences Laboratory at the Pacific Northwest National Laboratory of the Department of Energy as a Chief Scientist. He returned to China in 2017 and joined Fudan University as a Professor of Chemistry. He later moved to the School of Science at Westlake University, where he is currently a Full Professor of Chemistry. Prof. Wang's research interests include the structure and reaction dynamics of surface and interface, linear and nonlinear optical spectroscopy and modern analytical spectroscopy. He is mostly known for his "seminal contributions to the development of surface nonlinear vibrational spectroscopy and to the understanding of molecular interaction and structure at interfaces". His most cited paper so far is a systematic survey on the "Quantitative Spectral and Orientational Analysis in Surface Sum Frequency Generation Vibrational Spectroscopy (SFG-VS)". In recent years, he developed the sub-wavenumber high resolution broadband SFG-VS (HR-BB-SFG-VS) and demonstrated its ability for obtaining intrinsic and accurate spectral lineshape in SFG-VS and other nonlinear spectroscopic techniques, such as the Femtosecond Stimulated Raman Spectroscopy (FSRS).  Prof. Wang was selected into the Hundred Talents Program of the Chinese Academy Sciences (1999-2002) and was awarded the Distinguished Young Investigator Fund of the National Natural Science Foundation of China (2005-2008). In 2012, he was elected a Fellow of the American Physical Society. He was also selected into the High-Level Overseas Talents Program of Shanghai in 2017 and the National High-Level Overseas Talents Program of China in 2018. For Attendees' Attention Seating is on a first come, first served basis.  

Abstract Protein dynamics are fundamental to protein function and encode complex biomolecular mechanisms. Although Markov state models have made it possible to capture long-timescale protein conformational changes, many systems are still beyond their reach. In this lecture, the speaker will explore how incorporating dynamics memory (i.e., non-Markovian effects) into machine learning models can significantly reduce the computational cost of predicting long-time dynamics in these complex systems with high accuracy. First, the speaker will introduce the integrative-generalized master equation (IGME) method, which encodes non-Markovian dynamics through time-integrated memory kernels. Using IGME, his research team predicts multiple protein-protein interactions (PPIs) between KRAS and VHL, establishing a new workflow for PROTAC linker design in targeted protein degradation. One of the predictions matches well with the co-crystal structure, while these non-canonical PPIs remain challenging for AlphaFold3 to predict. Next, the speaker will present MEMnets, a deep-learning approach for identifying the slow collective variables (CVs) of protein dynamics. Unlike conventional deep learning models like VAMPnets, which assume Markovian dynamics, MEMnets builds on IGME theory with a novel loss function that minimizes the time integration of memory kernels. His research team demonstrates that MEMnets-derived CVs elucidate the molecular mechanisms behind the loading gate opening of bacterial RNA Polymerase (RNAP), revealing a transiently open cryptic pocket that binds the antibiotic Myx. Finally, the speaker will introduce the TS-DART method, which automatically identifies transition states (TS) across multiple free energy barriers in biomolecular systems. Inspired by Trustworthy AI, TS-DART detects TS as out-of-distribution data in a hyperspherical latent space. Using TS-DART, his research team successfully shows how antibiotic binding shifts the transition state, inhibiting bacterial RNAP elongation.   About the Speaker Prof. HUANG Xuhui obtained his Ph.D. from Columbia University in 2006 with Prof. Bruce BERNE. He did his postdoc research at Stanford University with Profs. Michael LEVITT and Vijay PANDE. He was an Assistant, Associate and Full Professor at The Hong Kong University of Science and Technology (HKUST) between 2010 and Summer 2021. Since Fall 2021, Prof. Huang took up the position of the Hirschfelder Endowed Chair Professor in Theoretical Chemistry, and Director of Theoretical Chemistry Institute at University of Wisconsin-Madison. He has received numerous awards, including the Theory & Computation Award for Mid-Career Scientists from Biophysical Society in 2023, Pople Medal from the Asia-Pacific Association of Theoretical and Computational Chemists in 2021, American Chemical Society OpenEye Outstanding Junior Faculty Award in 2014, and Hong Kong Research Grant Council Early Career Award in 2013. He is a founding member of The Hong Kong Young Academy of Sciences (YASHK) and a Fellow of The Royal Society of Chemistry (FRSC). His group pioneered in elucidating the dynamics of protein conformational changes by developing statistical-mechanics-based methods and machine learning tools.   For Attendees' Attention Seating is on a first come, first served basis.