Representations and strategies for transferable machine learning improve model performance in chemical discovery
Strategies for machine-learning(ML)-accelerated discovery that are general across materials composition spaces are essential, but demonstrations of ML have been primarily limited to narrow composition variations. By addressing the scarcity of data in promising regions of chemical space for challe...
Main Authors: | , , , , , |
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其他作者: | |
格式: | 文件 |
语言: | English |
出版: |
AIP Publishing
2022
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在线阅读: | https://hdl.handle.net/1721.1/145470 |