Publications

Selected

  1. DefectNet workflow demonstration

    A Foundation Model for Non-Destructive Defect Identification from Vibrational Spectra

    Mouyang Cheng†,*, Chu-Liang Fu†, Bowen Yu†, Eunbi Rha, Abhijatmedhi Chotrattanapituk, Douglas L. Abernathy, Yongqiang Cheng, and Mingda Li*

    † Equal contribution. * Corresponding author.

    Matter Published Mar 2026 · arXiv May 2025

    A non-destructive defect identification probe to predict the chemical identity and concentration of substitutional point defects directly from vibrational spectra

  2. Integrated workflow for XPCS, theory, and domain-adaptive machine learning in grain-boundary dynamics

    Probing Non-Equilibrium Grain Boundary Dynamics with XPCS and Domain-Adaptive Machine Learning

    Mouyang Cheng†,*, Bowen Yu†, Chu-Liang Fu, Nina Andrejevic, Matthias T. Agne, Riley Hanus, Qiwei Wan, Nathan C. Drucker, Thanh Nguyen, Andrei Fluerasu, Lutz Wiegart, Xiaoqian M Chen, Daniel Pajerowski, Yongqiang Cheng, Joshua J Turner, G. Jeffrey Snyder, and Mingda Li*

    † Equal contribution. * Corresponding author.

    In review arXiv May 2026

    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

  1. Integrated workflow for XPCS, theory, and domain-adaptive machine learning in grain-boundary dynamics

    Probing Non-Equilibrium Grain Boundary Dynamics with XPCS and Domain-Adaptive Machine Learning

    Mouyang Cheng†,*, Bowen Yu†, Chu-Liang Fu, Nina Andrejevic, Matthias T. Agne, Riley Hanus, Qiwei Wan, Nathan C. Drucker, Thanh Nguyen, Andrei Fluerasu, Lutz Wiegart, Xiaoqian M Chen, Daniel Pajerowski, Yongqiang Cheng, Joshua J Turner, G. Jeffrey Snyder, and Mingda Li*

    † Equal contribution. * Corresponding author.

    In review arXiv May 2026

    Pairing X-ray photon correlation spectroscopy (XPCS) with semi-supervised domain adaptation to quantify slow, non-equilibrium grain-boundary dynamics in nanocrystalline silicon

  2. RL-guided critical current workflow demonstration

    Reinforcement learning-guided optimization of critical current in high-temperature superconductors

    Mouyang Cheng†,*, Qiwei Wan†, Bowen Yu†, Eunbi Rha, Michael J. Landry, and Mingda Li*

    † Equal contribution. * Corresponding author.

    In review arXiv Oct 2025

    Combining reinforcement learning (RL) with time-dependent Ginzburg-Landau (TDGL) simulations to autonomously optimize defect configurations for high-temperature superconductors

  3. CrysVCD workflow demonstration

    Enhancing Materials Discovery with Valence Constrained Design in Generative Modeling

    Mouyang Cheng†,*, Weiliang Luo†, Hao Tang†, Bowen Yu, Yongqiang Cheng, Weiwei Xie, Ju Li, Heather J. Kulik, and Mingda Li*

    † Equal contribution. * Corresponding author.

    In review arXiv Jul 2025

    Integrating chemical valence constraints into the generative materials pipeline

  4. DefectNet workflow demonstration

    A Foundation Model for Non-Destructive Defect Identification from Vibrational Spectra

    Mouyang Cheng†,*, Chu-Liang Fu†, Bowen Yu†, Eunbi Rha, Abhijatmedhi Chotrattanapituk, Douglas L. Abernathy, Yongqiang Cheng, and Mingda Li*

    † Equal contribution. * Corresponding author.

    Matter Published Mar 2026 · arXiv May 2025

    A non-destructive defect identification probe to predict the chemical identity and concentration of substitutional point defects directly from vibrational spectra