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• Generative models (conditional denoising diffusion models, VAEs) for materials synthesis planning using molecular and crystalline materials datasets (Nanoporous materials synthesis planning: Work in progress; Inorganic materials synthesis planning: Paper | Code)

• Reinforcement learning (deep Q-learning, policy gradient) for inverse design of inorganic materials (Paper | Code)

• Materials representation learning (mutli-task transformer pretraining) for inorganic materials property/synthesis prediction (Paper | Code in progress)

• Model explainability/interpretability (Aggregated SHAP) for materials synthesis (Paper | Code)

• Natural language processing (automated few-shot learning) for scientific data extraction (Work in progress)

• Constrained RL for process optimization (Paper | Code)

• Bayesian optimization for chemistry/materials (Code for AC BO Hackathon)

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