Data Driven Strategies for the Discovery and Optimization of Next Generation Energy Materials

Data Driven Strategies for the Discovery and Optimization of Next Generation Energy Materials | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 21 July 2025 V1 Latest version Share on Data Driven Strategies for the Discovery and Optimization of Next Generation Energy Materials Author : Annower Molla 0009-0009-2217-5606 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175312780.02140957/v1 356 views 173 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The world is shifting toward clean and sustainable energy, and energy materials that are efficient, stable, and economically viable are in demand at an unprecedented rate. Traditionally, the search for these materials required 10-20 years of painstaking research, years of funding, and immense intellectual labor. Revealing energy materials that were efficient, stable economically upbeat was invariably a significant financial cost. Now with artificial intelligence (AI) already a reality in terms of machine learning and deep learning techniques, the discovery of new materials will be more rapid, easier, better, cheaper and more economically viable. Studies have suggested that AI has made it possible to accelerate materials processing by as much as 70%, while reducing costs of research and development by greater than 50%. AI models can, not only predict properties of materials but also optimize composition, design and simulate performance (even before the research is actually trialed) in areas like lithium-ion batteries, supercapacitors, fuel cells and photovoltaics. Energy materials screening platforms such as The Materials Project and AFLOW have already contributed to the screening of over 100,000 candidate materials. Rapidly developing neural networks, deep learning and graph-based predictive models (e.g, CGCNN) have made predictive analysis of properties easier imaginable and yielding more accurate and interpretable results. In summary this paper represents how AI is currently being used to foster the rapid and accessible discovery of energy materials, and the authors suggest that AI will have a large role to play in the science and engineering science revolution, going forward. Supplementary Material File (data driven strategies for the discovery and optimization of next generation energy.pdf) Download 240.87 KB Information & Authors Information Version history V1 Version 1 21 July 2025 Copyright This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License Keywords artificial intelligence computational chemistry energy materials machine learning materials science Authors Affiliations Annower Molla 0009-0009-2217-5606 [email protected] Department of Chemistry, Sister Nivedita University View all articles by this author Metrics & Citations Metrics Article Usage 356 views 173 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Annower Molla. Data Driven Strategies for the Discovery and Optimization of Next Generation Energy Materials. Authorea . 21 July 2025. 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