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.

DISSERTATION DEFENSE

Author: Peyton Chandarana

Advisor: Ramtin Zand

Date: March 23, 2026

Time: 1:00 PM

Place: Online/Room 2277,  Storey Innovation Center

Remote join (MS Teams):

Link: Peyton Chandarana: Dissertation Defense | Microsoft Teams

Meeting ID: 263 606 597 033 12

Passcode: DD6ts7Mg

Abstract

The pursuit of energy-efficient intelligence for constrained and always-on sensing environments has positioned neuromorphic computing as a pivotal alternative to conventional von Neumann architectures through its adoption of asynchronous and event-based computing inspired by the biological brain. Additionally, outside of these constrained environments, neuromorphic computing design principles can help alleviate the current power and efficiency dilemmas put forth by the rapidly growing AI industry. This dissertation presents research focused on the hardware-software co-design of spiking neural networks (SNNs), progressing from foundational signal encoding techniques to the deployment of complex, heterogeneous, and hybrid systems. We start by focusing on the deployment of practical workloads, such as American Sign Language recognition, on Intel’s Loihi neuromorphic platform. Benchmarking against standard edge accelerators demonstrates that neuromorphic paradigms achieve significant gains in energy efficiency and power reduction, maximizing runtime on edge devices deployed as assistive technologies and reducing the overall energy footprint for tasks without much accuracy degradation. We then explore the integration of spiking and non-spiking domains to leverage the unique advantages of each. We present an end-to-end co-design framework that utilizes SNNs for temporal feature extraction and artificial neural networks (ANNs) for high-precision classification. To facilitate this integration, we propose custom interface hardware, specifically an accumulator circuit, designed to synchronize asynchronous spike streams for synchronous edge processing. These co-design principles provide a blueprint for the next generation of neuromorphic capabilities by highlighting areas for improvement and how co-design principles can be expanded to create more capable and reliable autonomous systems while alleviating current problems faced by the immense scale and consumption of AI workloads.