2026-09-19T02:33:03Z https://api.figshare.com/v2/oai

oai:figshare.com:article/31451479 2025-12-01T00:00:00Z portal_693 item_type_8 month_year_12_2025

AI-Driven Discovery and Multiscale Computational Frameworks for Carbon-Based Materials Xiaoli Yan (420602) Engineering Materials Science This dissertation develops a unified, data-driven framework for carbon-based materials discovery that integrates generative artificial intelligence, multiscale simulations, and reactive dynamics to accelerate research central to carbon-to-X technologies. Chapter 2 establishes a one-directional workflow in which a generative AI model autonomously proposes novel metal–organic frameworks (MOFs) for carbon capture, followed by multiscale physics-based screening. 120,000 MOF AI-generated candidates are evaluated through an array of screening methods that validate structure and property of MOF, yielding 6 structures with CO2 uptakes exceeding 2 mol/kg at 0.1 bar and 300 K—demonstrating that generative design coupled with high-throughput validation can efficiently navigate the vast MOF design space. Chapter 3 extends this framework into a closed-loop, property-guided generative system. Here, an oracle-based scoring function provides feedback from machine learning predicted adsorption metric, dynamically steering the generative model toward desirable regions of the property space. This self-optimizing loop improves both discovery efficiency, physical fidelity, and design success rate. Chapter 4 transitions to nanocarbon systems, employing reactive molecular dynamics and machine-learning prediction to investigate the temperature- and pressure-dependent graphitization of nanodiamond surfaces, revealing atomistic pathways for sp3-to-sp2 transformation. Chapter 5 explores the co-pyrolysis of cellulose–graphene mixtures through reactive dynamics, coupled with non-equilibrium Green’s function (NEGF) formalism to quantify key molecular junctions for electron transport. Together, these studies unify porous, sp2, and hybrid carbon materials within a coherent methodological and conceptual framework. By combining forward generative design, feedback-driven optimization, and transport-level modeling, this work contributes a scalable paradigm for AI-accelerated carbon-materials innovation aligned with global carbon capture, utilization, and conversion goals. 2025-12-01T00:00:00Z Text Thesis 10.25417/uic.31451479.v1 https://figshare.com/articles/thesis/AI-Driven_Discovery_and_Multiscale_Computational_Frameworks_for_Carbon-Based_Materials/31451479 In Copyright