About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
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AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
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Presentation Title |
XenonPy: An Open Source Platform for Data-driven Materials Design with Small Data |
Author(s) |
Stephen Wu, Chang Liu, Ryo Yoshida |
On-Site Speaker (Planned) |
Stephen Wu |
Abstract Scope |
Machine learning has been proven to help accelerate materials discovery in different applications that have accumulated a large amount of data. However, there are many situations in materials science where the available data is limited due to lack of transparency of the specific field or extremely large search space of potential material candidates. Transfer learning is a machine learning technique that aims to improve learning efficiency of a target design task with little data by extracting useful knowledge from a relevant task with "big data". In this work, we developed an open source platform to perform transfer learning in materials science and also provide a large model database that serves as an open knowledge pool. Using this platform, we have made new material discoveries across different material types under different application scenarios, including both inorganic and organic compounds. |
Proceedings Inclusion? |
Planned: |
Keywords |
Computational Materials Science & Engineering, Machine Learning, |