Materials cartography: A forward-looking perspective on materials representation and devising better maps
Name
020901_1_5.0149804.pdf
Description
Published version
Size
3.92 MB
Format
Adobe PDF
Checksum (MD5)
34269680846e721a4c3dd6b1779cfe1e
Author(s)
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
Date Issued
June 1, 2023
Journal
APL Machine Learning
Publisher
AIP Publishing
Citation
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; Materials cartography: A forward-looking perspective on materials representation and devising better maps. APL Mach. Learn. 1 June 2023; 1 (2): 020901.
Version
Final published version
Abstract
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 for scientific research. In this Perspective, we discuss a few central challenges faced by ML practitioners in developing meaningful representations, including handling the complexity of real-world industry-relevant materials, combining theory and experimental data sources, and describing scientific phenomena across timescales and length scales. We present several promising directions for future research: devising representations of varied experimental conditions and observations, the need to find ways to integrate machine learning into laboratory practices, and making multi-scale informatics toolkits to bridge the gaps between atoms, materials, and devices.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology. Department of Mathematics
Massachusetts Institute of Technology. Research Laboratory of Electronics
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1063/5.0149804
