arXiv subjects

Victor Chang Lee

Publications and source records attributed to Victor Chang Lee.

5 recordsLinked to original sources

arXiv2026

Can Autonomous LLM Agents Execute Multireference Quantum Chemistry Calculations?

Multireference electronic-structure calculations remain difficult to automate because critical workflow decisions, including active-space selection, state averaging, convergence recovery, and state identification, traditionally rely on expert judgment. Here, we investigate whether an autonomous large language model (LLM) agent can perform these tasks without human intervention. The agent selects active spaces using literature-grounded analogies or explicitly documented chemical reasoning, generates and submits ORCA calculations, analyzes outputs, and records all decisions in an auditable reasoning log. Benchmarking against 558 vertical transition energies (VTEs) from QUESTDB shows that an unguided baseline agent achieves 24.9% coverage with a mean absolute error (MAE) of 0.373 eV. Introducing a structured decision ladder increases coverage to 44.1% while reducing the MAE to 0.339 eV. The largest gains are observed for double and Rydberg excitations, demonstrating that expert-informed procedural guidance substantially improves active-space construction and state identification. When provided with complete workflow information, the agent successfully reproduces published QUEST calculations with an MAE of only 23 meV, resolving 75% of target configurations within seven attempts. These results demonstrate that contemporary LLM agents can autonomously execute and reproduce complex multireference quantum-chemical workflows, while highlighting the importance of structured reasoning frameworks for achieving reliable high-throughput and high-fidelity electronic-structure calculations.

physics.chem-ph↗

arXiv2025

Isostructural electronic transition in MoS$_2$ probed by solid-state high harmonic generation spectroscopy

Studying materials under extreme pressure in diamond anvil cells (DACs) is key to discovering new states of matter, yet no method currently allows the direct measurement of the electronic structure in this environment. Solid-state high harmonic generation (sHHG) offers a new all-optical window into the electronic structure of materials. We demonstrate sHHG spectroscopy inside a DAC by probing $2H$-MoS$_2$, up to 30 GPa, revealing a pressure-induced crossover of the lowest direct bandgap from the $\textbf{K}$-point to the $Γ$-point. This transition manifests as a sharp minimum in harmonic intensity and a 30° rotation of the sHHG polarization anisotropy, despite the absence of a structural phase change. First-principles simulations attribute these features to interference between competing excitation pathways at distinct points in the Brillouin zone. Our results establish sHHG as a sensitive probe of electronic transitions at high pressure, enabling access to quantum phenomena that evade detection by conventional techniques.

cond-mat.mtrl-sci↗

arXiv2025

Moiré-Tunable Localization of Simultaneous Type I and Type II Band Alignment in a MoSe2/WS2 Heterobilayer

Moiré heterobilayers exhibiting spatially varying band alignment and electron and hole localization that can be precisely controlled through the twist angle have emerged as exciting platforms for studying complex quantum phenomena. While most heterobilayers of transition metal dichalcogenides (TMDs) have a type II band alignment, the introduction of type I band alignment could enable stronger light-matter coupling and enhanced radiative emission. Here, we show through a combination of first-principles GW plus Bethe Salpeter equation (GW-BSE) calculations and time- and angle-resolved photoemission spectroscopy (tr-ARPES) measurements that contrary to previous understanding, the MoSe2/WS2 heterobilayer has a type I band alignment at large twist angles and simultaneous regions of type I and type II band alignment due to the structural reconstruction in different high symmetry regions at small twist angles. In tr-ARPES, consistent with our calculations, a long-lived electron population is only observed in MoSe2 for samples with large twist angles, while in samples with small twist angles, signals from two distinct long-lived excitons are observed. Moreover, despite the near degeneracy of the conduction bands of the two layers, no excitonic hybridization occurs, suggesting that previously observed absorption peaks in this material arise from lattice reconstruction. Our findings clarify the complex energy landscape in MoSe2/WS2 heterostructures, where the coexistence of type I and type II band alignment opens the door to moiré-tunable optoelectronic devices with intrinsic lateral heterojunctions.

cond-mat.mtrl-sci↗

arXiv2025

Data-driven Low-rank Approximation for Electron-hole Kernel and Acceleration of Time-dependent GW Calculations

Many-body electron-hole interactions are essential for understanding non-linear optical processes and ultrafast spectroscopy of materials. Recent first principles approaches based on nonequilibrium Green's function formalisms, such as the time-dependent adiabatic GW (TD-aGW) approach, can predict the nonequilibrium dynamics of excited states including electron-hole interactions. However, the high dimensionality of the electron-hole kernel poses significant computational challenges for scalability. Here, we develop a data-driven low-rank approximation for the electron-hole kernel, leveraging localized excitonic effects in the Hilbert space of crystalline systems. Through singular value decomposition (SVD) analysis, we show that the subspace of non-zero singular values, containing the key information of the electron-hole kernel, retains a small size even as the k-grid grows, ensuring computational feasibility with extremely dense k-grids for converged calculations. Utilizing this low-rank property, we achieve at least 95% compression of the kernel and an order-of-magnitude speedup of TD-aGW calculations. Our method, rooted in physical interpretability, outperforms existing machine learning approaches by avoiding intensive training processes and eliminating time-accumulated errors, providing a general framework for high-throughput, nonequilibrium simulation of light-driven dynamics in materials.

physics.comp-ph↗

arXiv2023

Probabilistic forecast of nonlinear dynamical systems with uncertainty quantification

Data-driven modeling is useful for reconstructing nonlinear dynamical systems when the underlying process is unknown or too expensive to compute. Having reliable uncertainty assessment of the forecast enables tools to be deployed to predict new scenarios unobserved before. In this work, we first extend parallel partial Gaussian processes for predicting the vector-valued transition function that links the observations between the current and next time points, and quantify the uncertainty of predictions by posterior sampling. Second, we show the equivalence between the dynamic mode decomposition and the maximum likelihood estimator of the linear mapping matrix in the linear state space model. The connection provides a {probabilistic generative} model of dynamic mode decomposition and thus, uncertainty of predictions can be obtained. Furthermore, we draw close connections between different data-driven models for approximating nonlinear dynamics, through a unified view of generative models. We study two numerical examples, where the inputs of the dynamics are assumed to be known in the first example and the inputs are unknown in the second example. The examples indicate that uncertainty of forecast can be properly quantified, whereas model or input misspecification can degrade the accuracy of uncertainty quantification.

stat.ME↗