DISSERTATION DEFENSE

Author : Qinyang Li

Advisor: Dr. Jianjun Hu

Date: March 18, 2026

Time: 1:20PM

Place: Online/Room 2267,  Storey Innovation Center

Remote join (ZOOM):

Link: https://sc-edu.zoom.us/j/4997546955

Abstract

The discovery and optimization of advanced materials are central to addressing global challenges in energy, healthcare, and sustainability. This dissertation develops representation-aware and distribution-aware machine learning frameworks to improve robustness, generalization, and interpretability in materials informatics and radiation detection. The work spans crystal structure mapping, adversarial learning for out-of-distribution prediction, foundation-model-based property prediction, and deep neural classification of photon interactions. A global mapping framework is first introduced to analyze the inorganic materials space using compositional, structural, physical, and neural descriptors derived from the Materials Project database. By embedding materials into low-dimensional manifolds, the framework reveals clustering behavior and structure–property relationships, enabling systematic exploration of underrepresented material families.

To address distributional fragility in materials property prediction, the Crystal Adversarial Learning (CAL) algorithm is developed. CAL synthesizes adversarial samples in high-uncertainty regions and incorporates stability-aware training objectives, improving generalization under covariate, prior, and relation shifts. Experimental results demonstrate enhanced robustness in data-scarce regimes typical of experimental materials research.

The dissertation further investigates in-context foundation models for data-efficient property prediction. By integrating a pretrained tabular transformer with compositional descriptors and graph-derived structural embeddings, the proposed framework achieves competitive performance on the MatBench benchmark suite and on lattice thermal conductivity prediction without task-specific fine-tuning. Representation analyses indicate that foundation-model adaptation reorganizes latent feature spaces to better align with physical property gradients, particularly in small-to-medium data regimes.

Finally, a deep learning framework is applied to gamma-photon interaction classification in room-temperature semiconductor detectors. The proposed model distinguishes Compton scattering and photoelectric events from pulse waveforms with high accuracy and robustness across varying noise and energy conditions, demonstrating the transferability of representation-learning principles to signal-level scientific data.

Collectively, this work advances machine learning methodologies that integrate representation geometry, distributional robustness, and physical interpretability across heterogeneous scientific domains. The developed approaches provide scalable and interpretable tools for accelerating materials discovery and improving radiation detection systems.