Title: From Light to Learning: Predicting Excited-State Dynamics at the Spectroscopic Frontier
Speaker: Prof. Diana Y. Qiu (Yale University)
Time: 15:30-16:30, July 7 (Tue.), 2026
Place: NCTS Physics 3F R307, Cosmology Hall, NTU

Abstract:
Processes ranging from photosynthesis, to photocatalysis, to energy harvesting in photovoltaic cells all begin in the same way: the absorption of light creates an exciton—a correlated electron-hole pair that carries energy rather than charge. Exciton dynamics and coherences determine the efficiency of energy harvesting and transport, while excitonic manipulation enables the optical preparation and transduction of quantum states and offers the potential to integrate the fast speed of photons into electronics. However, quantitative predictions of exciton and other excited-state dynamics remain a significant challenge. The first-principles understanding of exciton dynamics requires a few basic building blocks: 1) The full exciton dispersion to capture the phase space of momentum and energy conserving scattering processes, 2) Interaction of excitons with external perturbations such as electromagnetic fields and lattice vibrations, and 3) An equation of motion describing the dynamical processes. In this talk, I will discuss some of my group’s recent developments in these directions. Firstly, we have recently measured the exciton dispersion in a 2D materials revealing for the first time the emergence of a massless excitons composed of massive electrons and holes [1]. Secondly, I will show how excitons play a surprising role in nonlinear optics beyond the perturbative regime [2]. Finally, I will discuss how machine learning is opening new possibilities for simulating excited states far beyond the reach of conventional calculations [3,4].

[1] L. Liu, S.Y. Woo, J. Wu, B. Hou, C. Su, D.Y. Qiu, “Direct Observation of Massless Excitons and Linear Exciton Dispersion,” Nature Physics (2026).
[2] V. Chang Lee, L. Yue, M.B. Gaarde, Y.-H. Chan, D.Y. Qiu, “Many-body enhancement of high harmonic generation in monolayer MoS2,” Nature Comm. 15, 6228 (2024).
[3] B. Hou, J. Wu, D.Y. Qiu, "Unsupervised representation learning of Kohn–Sham states and consequences for downstream predictions of many-body effects," Nature Comm. 15, 9481 (2024).
[4] B. Hou, X. Xu, J. Wu, D.Y. Qiu, "MBFormer: A General Transformer-based Learning Paradigm for Many-body Interactions in Real Materials," arXiv:2507.05480 (2025).