1. AI-driven identification of a novel malate structure from recycled lithium-ion batteries.
- Author
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Zanoletti A, Cornelio A, Galli E, Scaglia M, Bonometti A, Zacco A, Depero LE, Gianoncelli A, and Bontempi E
- Subjects
- Lithium chemistry, Electric Power Supplies, Recycling, Artificial Intelligence, Malates chemistry
- Abstract
The integration of Artificial Intelligence (AI) into the discovery of new materials offers significant potential for advancing sustainable technologies. This paper presents a novel approach leveraging AI-driven methodologies to identify a new malate structure derived from the treatment of spent lithium-ion batteries. By analysing bibliographic data and incorporating domain-specific knowledge, AI facilitated the identification and structure refinement of a new malate complex containing different metals (Ni, Mn, Co, and Cu). The synthesized compound was investigated through chemical and physical analyses, confirming its unique structure and composition. The present work proposes a significant difference from the classical use of AI in materials science, typically rooted in data-driven approaches relying on extensive datasets. This hybrid approach, combining AI's computational power with human expertise, not only expedited the structure determination process but also ensured the reliability and accuracy of the results. Finally, AI-driven material discovery highlights that waste materials can be transformed into valuable chemical products, suggesting their possible reuse, with several expected benefits, emphasising the role of AI in fostering not only innovation but also sustainability in material science., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)
- Published
- 2025
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