Publications
Selected
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A Foundation Model for Non-Destructive Defect Identification from Vibrational Spectra
† Equal contribution. * Corresponding author.
Matter
A non-destructive defect identification probe to predict the chemical identity and concentration of substitutional point defects directly from vibrational spectra
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Probing Non-Equilibrium Grain Boundary Dynamics with XPCS and Domain-Adaptive Machine Learning
† Equal contribution. * Corresponding author.
In review
Pairing X-ray photon correlation spectroscopy (XPCS) with semi-supervised domain adaptation to quantify slow, non-equilibrium grain-boundary dynamics in nanocrystalline silicon
All publications
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Probing Non-Equilibrium Grain Boundary Dynamics with XPCS and Domain-Adaptive Machine Learning
† Equal contribution. * Corresponding author.
In review
Pairing X-ray photon correlation spectroscopy (XPCS) with semi-supervised domain adaptation to quantify slow, non-equilibrium grain-boundary dynamics in nanocrystalline silicon
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Reinforcement learning-guided optimization of critical current in high-temperature superconductors
† Equal contribution. * Corresponding author.
In review
Combining reinforcement learning (RL) with time-dependent Ginzburg-Landau (TDGL) simulations to autonomously optimize defect configurations for high-temperature superconductors
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Enhancing Materials Discovery with Valence Constrained Design in Generative Modeling
† Equal contribution. * Corresponding author.
In review
Integrating chemical valence constraints into the generative materials pipeline
-
A Foundation Model for Non-Destructive Defect Identification from Vibrational Spectra
† Equal contribution. * Corresponding author.
Matter
A non-destructive defect identification probe to predict the chemical identity and concentration of substitutional point defects directly from vibrational spectra