Past, present, prospect: AI‐driven evolution of low‐dimensional material design for sustainable environmental solutions
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Le résumé fourni par la source
Abstract Low‐dimensional materials have attracted significant interest for their unique properties, including high surface area, confined but tunable electronics and superior catalysis, making them ideal for environmental applications. Their potential to address key challenges in solar energy conversion and in‐situ remediation highlights their importance in advancing environmental sustainability. However, traditional methods of low‐dimensional material design face significant obstacles, such as scalability limitations, high computational costs, and the inherent difficulty in accurate prediction of material properties, underscoring the need for innovative approaches. Here, we demonstrate an AI‐driven evolution of low‐dimensional material design for sustainable environmental solutions, from the traditional techniques in the past, through the present transition to computational approaches, to the prospect where AI‐enabled strategies exhibit the supremacy. This review covers properties of low‐dimensional materials and the fundamental design principles, emphasizing the pivotal role of deep learning in optimizing and accelerating design of advanced functional materials. Further explorations focus on their applications for sustainable environmental solutions, including pollution remediation, water purification, nitrogen fixation, CO 2 reduction as well as hydrogen and hydrogen peroxide production. Ultimately, the key challenges and future trends are identified in the aspects of algorithm, intelligence and scalability for environmental applications. This work offers a comprehensive overview on the evolution pathway of design strategies for low‐dimensional materials driven by AI methodology, demonstrating transformative insights that not only accelerate the discovery of low‐dimensional materials, but also motivate the environmental applications in various domains.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Past, present, prospect: AI‐driven evolution of low‐dimensional material design for sustainable environmental solutions
- Date Crossref
- 29/09/2025
- Éditeur
- Wiley
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Dalian Maritime University pays non établi dans la noticeUniversité ou école supérieure
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Nanyang Technological University pays non établi dans la noticeUniversité ou école supérieure
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Southeast University pays non établi dans la noticeUniversité ou école supérieure
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Institute of Agricultural Resources and Regional Planning pays non établi dans la noticeStructure de recherche
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Chinese Academy of Agricultural Sciences pays non établi dans la noticeOrganisme public
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College of Information Science and Technology pays non établi dans la noticeUniversité ou école supérieure
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School of Civil and Environmental Engineering pays non établi dans la noticeUniversité ou école supérieure
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School of Transportation pays non établi dans la noticeUniversité ou école supérieure
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Institute of Agriculture Resources and Regional Planning pays non établi dans la noticeStructure de recherche
Dalian Maritime University, Nanyang Technological University et Southeast University, avec 6 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.