Data-Driven and Machine Learning Methods for Green Energy Materials
A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Materials Processes".
Deadline for manuscript submissions: 10 January 2027 | Viewed by 3
Special Issue Editors
Interests: material informatics; machine learning; metallic glass; data-driven material design; nanomaterials
Special Issue Information
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
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- machine learning
- materials informatics
- active learning
- high-throughput experiments
- generative/inverse design
- batteries and electrolytes
- electrocatalysis and fuel cells
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