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...

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书目详细资料
Main Authors: Harper, Daniel R, Nandy, Aditya, Arunachalam, Naveen, Duan, Chenru, Janet, Jon Paul, Kulik, Heather J
其他作者: Massachusetts Institute of Technology. Department of Chemistry
格式: 文件
语言:English
出版: AIP Publishing 2022
在线阅读:https://hdl.handle.net/1721.1/145470