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: Steven B. Torrisi, Martin Z. Bazant, Alexander E. Cohen, Min Gee Cho, Jens S. Hummelshøj, Linda Hung, Gaurav Kamat, Arash Khajeh, Adeesh Kolluru, Xiangyun Lei, Handong Ling, Joseph H. Montoya, Tim Mueller, Aini Palizhati, Benjamin A. Paren, Brandon Phan, Jacob Pietryga, Elodie Sandraz, Daniel Schweigert, Yang Shao-Horn, Amalie Trewartha, Ruijie Zhu, Debbie Zhuang, Shijing Sun
Format: Article
Language:English
Published: AIP Publishing LLC 2023-06-01
Series:APL Machine Learning
Online Access:http://dx.doi.org/10.1063/5.0149804

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http://dx.doi.org/10.1063/5.0149804

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