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The materials experiment knowledge graph

Published:2023 Issue:4 Volume:2 Page:909-914
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Statt Michael J.¹,Rohr Brian A.¹,Guevarra Dan²³,Breeden Ja'Nya³,Suram Santosh K.⁴^ORCID,Gregoire John M.²³^ORCID

Affiliation:

1. Modelyst LLC, Palo Alto, CA 94306, USA

2. Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA 91125, USA

3. Liquid Sunlight Alliance, California Institute of Technology, Pasadena, CA, 91125, USA

4. Toyota Research Institute, Los Altos, CA 94022, USA

Abstract

Graph representations of hierarchical knowledge, including experiment provenances, will help usher in a new era of data-driven materials science.

Funder

Basic Energy Sciences

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2023/DD/D3DD00067B

Reference41 articles.

1. Materials Acceleration Platforms: On the way to autonomous experimentation

2. The case for data science in experimental chemistry: examples and recommendations

3. Artificial intelligence for materials discovery

4. Autonomous experimentation systems for materials development: A community perspective

5. Toward autonomous materials research: Recent progress and future challenges

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1. A materials terminology knowledge graph automatically constructed from text corpus;Scientific Data;2024-06-07

2. Designing workflows for materials characterization;Applied Physics Reviews;2024-03-01

3. MatKG: An autonomously generated knowledge graph in Material Science;Scientific Data;2024-02-17

4. A dynamic knowledge graph approach to distributed self-driving laboratories;Nature Communications;2024-01-23

5. Chemistry in a graph: modern insights into commercial organic synthesis planning;Digital Discovery;2024

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