소재 AI 모델링

Materials AI Modeling Laboratory develops AI-assisted synthesis planning, multiscale modeling, and nucleation control workflows for semiconductors, ferroics, topological materials, and battery systems. We develop AI-assisted workflows that integrate synthesis knowledge extracted from the literature, physics- and simulation-informed descriptors, and experimental data to identify promising process conditions for experimental validation. Our research focuses on transforming heterogeneous knowledge from publications, simulations, and experiments into structured synthesis datasets; developing predictive models for synthesis outcomes and material properties; and prioritizing experimentally testable process conditions. Synthesis Knowledge Structuring: Extract and standardize materials, processing conditions, characterization results, and synthesis outcomes from publications and experimental records. Synthesis and Property Modeling: Develop and validate data-driven models that relate processing variables to synthesis feasibility and target properties. Candidate Prioritization: Combine predictive search, domain knowledge, and optimization methods to rank candidate process conditions for experimental validation. Prioritized Candidates: Ranked process-condition candidates for targeted materials systems. Interpretable Design Insights: Quantitative relationships between synthesis variables, predicted outcomes, and material-performance trends. Iterative Experiment-Model Learning: Workflows that incorporate experimental feedback to refine models and guide subsequent experiments. AI-guided discovery, multiscale simulation, and nucleation-aware process modeling for advanced materials and devices. Computational materials science from atomic-scale mechanisms to process and device-level de