AI-Driven Molecular Discovery and Design for Energy Materials and Applications
A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".
Deadline for manuscript submissions: 30 September 2026 | Viewed by 154
Special Issue Editor
Special Issue Information
Dear Colleagues,
The rapid and efficient development of energy-related chemicals is crucial for a sustainable future. However, the discovery and optimization of novel materials or molecules are often hindered by the complexity of the vast design space. The integration of artificial intelligence (AI) offers transformative potential, enabling accelerated material discovery, prediction of properties, optimization of synthesis, and enhancement of device performance across various energy technologies.
This Special Issue aims to collect cutting-edge research and reviews at the intersection of AI-related development of energy materials/chemistry. We seek contributions that leverage AI for science to tackle molecular-level challenges in energy storage, conversion, and management. We welcome submissions covering, but not limited to, AI-boosted development of batteries, fuel cells, solar cells, phase change materials for thermal storage, and related devices. Submissions should also emphasize the development or application of AI-related algorithms or methodology for modeling, design, characterization, and data analysis in these domains.
We are pleased to invite you to contribute your work to this Special Issue, which includes but is not limited to
- AI-driven optimization of synthesis and manufacturing processes of energy-application-related molecules (e.g., electrolyte molecules, catalyst precursors).
- Predictive modeling of molecular properties (e.g., solubility, redox potential) and their correlation with device performance.
- Multi-scale modeling bridging molecular structures, material microstructures to system-level energy performance.
- AI for experimental data analysis of molecular characterization and autonomous laboratories for energy molecular research.
- Application-focused studies on molecular design for batteries, supercapacitors, fuel cells, photocatalysts, electrocatalysts, and thermal energy storage systems.
This Special Issue aims to collect AI-assisted discovery/design of energy materials (e.g., electrodes, catalysts, electrolytes, etc.) and applications (e.g., batteries, fuel cells, heat-storage devices, etc.) at the molecular level. In this Special Issue, original research articles and reviews are welcome.
I/We look forward to receiving your contributions.
Dr. Ruijie Zhu
Guest Editor
Manuscript Submission Information
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Keywords
- artificial intelligence
- machine learning
- energy materials
- batteries
- fuel cells
- solar cells
- energy storage
- computational chemistry
- materials design
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