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(Late) Invited Presentation

Chemometrics

CHEM01: Old Codes, New Tricks: A Chemometric Odyssey

Utilizing Predictive Analysis to Optimize Hyperspectral Image Analysis for Strain Engineering of 2D Material

Monday, October 6, 2025
1:50 PM - 2:10 PM EDT
Location: Meeting Room #7
In fifteen words or less, explain the significance of this contribution (Novel Aspect).: Predictive analysis for strain engineering of 2D materials.

Abstract Text:

Strain engineering in two-dimensional (2D) materials provides a powerful pathway for realizing single photon emitters, a key component for future quantum technologies. Unlike their bulk counterparts, 2D materials display highly tunable optoelectronic properties, where the bandgap can shift significantly with layer number and applied strain. Quantifying these effects typically requires finite element simulations, which are accurate but computationally expensive and limit rapid exploration of device geometries. In this work, we present a machine learning assisted methodology that reduces the reliance on large scale simulations. By training ML models on a combination of simulated strain profiles and experimental photoluminescence measurements, we can (1) identify viable structures to perform costly simulations on and (2) predict local strain distributions with reduced computational cost. This approach enables faster, more scalable design of strain engineered quantum emitter platforms and provides a generalizable strategy for integrating ML into the fabrication and characterization workflow.