Interpretable Machine Learning Enabled Inorganic Reaction Classification and Synthesis Condition Prediction-Reference-Cited by-同舟云学术

Interpretable Machine Learning Enabled Inorganic Reaction Classification and Synthesis Condition Prediction

Published:2023-01-27 Issue:3 Volume:35 Page:1062-1079
ISSN:0897-4756
Container-title:Chemistry of Materials
language:en
Short-container-title:Chem. Mater.

Author:

Karpovich Christopher¹^ORCID,Pan Elton¹,Jensen Zach¹^ORCID,Olivetti Elsa¹^ORCID

Affiliation:

1. Department of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, Massachusetts02139, United States

Funder

Division of Graduate Education

Advanced Research Projects Agency - Energy

Office of Naval Research

Division of Materials Research

Publisher

American Chemical Society (ACS)

Subject

Materials Chemistry,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.chemmater.2c03010

Reference100 articles.

1. Machine Learning for Catalysis Informatics: Recent Applications and Prospects

2. Identification of advanced spin-driven thermoelectric materials via interpretable machine learning

3. Rapid and Accurate Machine Learning Recognition of High Performing Metal Organic Frameworks for CO2 Capture

4. Data-driven materials research enabled by natural language processing and information extraction

5. Machine-learned and codified synthesis parameters of oxide materials

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