Hogeun Chang | Searcharxiv

arXiv2026

Single-Emitter Spectra from an Ensemble

The heterogeneity in nanoscale emitters hinders efforts to understand their basic photophysics and limits their use in practical applications. Existing methods have difficulty accurately characterizing single-emitter spectra and optical heterogeneity on a statistical scale. Here, we introduce SPICEE (SPectrally Imbalanced Correlations from Ensemble Emission), a spectrally filtered photon-correlation technique that recovers single-particle emission lineshapes from an ensemble sample. Analytical derivations, numerical modeling, and experiments on a solution ensemble of emitters validate the technique. We apply SPICEE to blue-emitting ZnSeTe semiconductor nanocrystals relevant to display applications and find that the low color purity in the ensemble spectrum is primarily caused by a small subpopulation of nanocrystals with a distinct emission mechanism. This work demonstrates that SPICEE is a powerful high-throughput tool for accurately characterizing the single-emitter properties of nanoscale systems.

cond-mat.mes-hall↗

arXiv2025

Aligning Reasoning LLMs for Materials Discovery with Physics-aware Rejection Sampling

AI-driven materials discovery that couples automated experimentation with algorithmic decision-making requires process aware recipe to property predictors that are accurate, calibrated, and physically admissible. We approach this as a reasoning problem with large reasoning models (LRMs). To instill reasoning capability into language models, we curate reasoning traces from a teacher model to train a student model. However, most training pipelines select reasoning traces using binary correctness or learned preference signals that poorly reflect physical admissibility. We introduce Physics-aware Rejection Sampling (PaRS), a training-time trace selection scheme that favors traces consistent with fundamental physics and numerically close to targets, with lightweight halting to control compute. We instantiate our framework with a large student model fine-tuned on traces synthesized by a larger teacher model, and evaluate under matched token budgets against various rejection sampling baselines. Our method improves accuracy and calibration, reduces physics-violation rates, and lowers sampling cost relative to baselines. These results indicate that modest, domain-aware constraints combined with trace-level selection provide a practical path toward reliable, efficient LRMs for process-aware property prediction and closed-loop materials design.

cs.AI↗