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...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
AIP Publishing
2024
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| Online Access: | https://hdl.handle.net/1721.1/154283 |
| _version_ | 1824458260653015040 |
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| author | 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 |
| author2 | Massachusetts Institute of Technology. Department of Chemical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Chemical Engineering 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 |
| author_sort | Torrisi, Steven B. |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T13:50:01Z |
| format | Article |
| id | mit-1721.1/154283 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2025-02-19T04:23:04Z |
| publishDate | 2024 |
| publisher | AIP Publishing |
| record_format | dspace |
| spelling | mit-1721.1/1542832025-01-07T04:37:43Z Materials cartography: A forward-looking perspective on materials representation and devising better maps 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 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 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. 2024-04-25T14:42:34Z 2024-04-25T14:42:34Z 2023-06-01 2024-04-25T14:35:20Z Article http://purl.org/eprint/type/JournalArticle 2770-9019 https://hdl.handle.net/1721.1/154283 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. en 10.1063/5.0149804 APL Machine Learning Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ application/pdf AIP Publishing AIP Publishing |
| spellingShingle | 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 Materials cartography: A forward-looking perspective on materials representation and devising better maps |
| title | Materials cartography: A forward-looking perspective on materials representation and devising better maps |
| title_full | Materials cartography: A forward-looking perspective on materials representation and devising better maps |
| title_fullStr | Materials cartography: A forward-looking perspective on materials representation and devising better maps |
| title_full_unstemmed | Materials cartography: A forward-looking perspective on materials representation and devising better maps |
| title_short | Materials cartography: A forward-looking perspective on materials representation and devising better maps |
| title_sort | materials cartography a forward looking perspective on materials representation and devising better maps |
| url | https://hdl.handle.net/1721.1/154283 |
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