Data-Driven and Machine Learning Methods for Green Energy Materials

Dear Colleagues,

Accelerating the discovery, optimization, and deployment of green energy materials requires approaches that are simultaneously data-rich, physics-grounded, and readily translatable to manufacturing. This Special Issue seeks contributions that leverage data-driven and machine learning (ML) methods—alone or tightly coupled with theory and experimentation—to advance materials for sustainable energy generation, conversion, storage, transport, and circularity. We particularly welcome studies spanning atoms-to-systems length and time scales, including high-throughput computation/experimentation; surrogate and physics-informed models; closed-loop (autonomous) discovery; uncertainty quantification; and multi-objective optimization that jointly considers performance, durability, cost, and environmental impact (e.g., embodied carbon and critical material intensity).

Topics of interest include the following (non-exhaustive):

• ML and material informatics for photovoltaics, photocatalysts, electrocatalysts (e.g., CO₂/H₂O conversion), batteries and solid-state electrolytes, fuel cells/electrolyzers, thermoelectrics, hydrogen production/storage, membranes, and sorbents/adsorbents.
• Structure–property–processing relationships learned from multi-fidelity data (DFT/MD/phase-field/continuum + experiment), transfer learning, and domain adaptation.
• Physics-informed, graph-based, and generative models and inverse design and constrained search.
• Autonomous labs, Bayesian/active learning, and closed-loop synthesis/characterization with real-time decision-making.
• Robustness, interpretability, and uncertainty management in material ML and FAIR data practices, benchmark datasets, and reproducible workflows (code/data/model sharing).
• Digital twins and process–material co-optimization linking microstructure to device/module- and system-level performance and reliability.
• Lifecycle-aware material and process design, including recycling/upcycling, degradation modeling, and circular economy analytics.

Dr. Guannan Liu
Prof. Dr. Akeel Shah
Guest Editors

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