Materials cartography: A forward-looking perspective on materials representation and devising better maps

Materials cartography: A forward-looking perspective on materials representation and devising better maps

Machine learning (ML) is gaining popularity as a tool for materials scientists to accelerate computation, automate data analysis, and predict materials properties. The representation of input material features is critical to the accuracy, interpretability, and generalizability of data-driven models...

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Bibliographic Details
Main Authors: Torrisi, Steven B., Bazant, Martin Z., Cohen, Alexander E., Cho, Min Gee, Hummelshøj, Jens S., Hung, Linda, Kamat, Gaurav, Khajeh, Arash, Kolluru, Adeesh, Lei, Xiangyun, Ling, Handong, Montoya, Joseph H., Mueller, Tim, Palizhati, Aini, Paren, Benjamin A., Phan, Brandon, Pietryga, Jacob, Sandraz, Elodie, Schweigert, Daniel, Shao-Horn, Yang, Trewartha, Amalie, Zhu, Ruijie, Zhuang, Debbie, Sun, Shijing
Other Authors: Massachusetts Institute of Technology. Department of Chemical Engineering
Format: Article
Language:English
Published: AIP Publishing 2024
Online Access:https://hdl.handle.net/1721.1/154283

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https://hdl.handle.net/1721.1/154283

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