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simulations. Data-driven materials discovery: ML models for property prediction, materials design, or synthesis optimization. AI/ML methods development: Neural networks, graph neural networks (GNNs), generative
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optimization. AI/ML methods development: Neural networks, graph neural networks (GNNs), generative AI, or active learning for materials applications. Integration of theory and experiment: Using computation and
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simulations. Data-driven materials discovery: ML models for property prediction, materials design, or synthesis optimization. AI/ML methods development: Neural networks, graph neural networks (GNNs), generative
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optimization. AI/ML methods development: Neural networks, graph neural networks (GNNs), generative AI, or active learning for materials applications. Integration of theory and experiment: Using computation and