DISSERTATION DEFENSE

Author : Rongzhi Dong

Advisor: Dr. Jianjun Hu

Date: March 23, 2026

Time: 10:00AM

Place: Online/Room 2265,  Storey Innovation Center

Remote join (ZOOM):

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

Abstract

The accelerated discovery of novel functional materials is critical for advancing transformative technologies in energy storage, electronics, and catalysis, yet current strategies remain fundamentally constrained by the limited size of existing materials databases and the difficulty of building predictive models that generalize to unseen compounds. This dissertation addresses these challenges through five interconnected deep learning and machine learning studies. First, a diffusion language model framework is proposed for the generative design of novel inorganic materials, with DFT validation confirming the thermodynamic stability of newly identified compounds. Second, generative modeling is extended to two-dimensional (2D) materials discovery, producing diverse and stable candidates that substantially expand the known structural landscape of this emerging materials class. Third, CondADiT, a composition-conditioned latent diffusion framework, is introduced for crystal structure prediction directly from chemical composition, achieving state-of-the-art performance on multiple benchmarks. Fourth, DeepXRD is presented as a deep learning framework for predicting X-ray diffraction spectra directly from composition, enabling scalable structural inference without costly simulations or experimental measurements. Fifth, domain adaptation techniques are systematically evaluated for materials property prediction under realistic distribution shifts, demonstrating significant improvements in out-of-distribution generalization. Together, these contributions establish a comprehensive data-driven framework that integrates generative modeling, structure learning, and domain-adaptive prediction to accelerate the discovery of stable, synthesizable, and functionally diverse materials.