Nanomaterial Synthesis Insights from Machine Learning of Scientific Articles by Extracting, Structuring, and Visualizing Knowledge-Reference-Cited by-同舟云学术

Nanomaterial Synthesis Insights from Machine Learning of Scientific Articles by Extracting, Structuring, and Visualizing Knowledge

Published:2020-04-14 Issue:6 Volume:60 Page:2876-2887
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Hiszpanski Anna M.¹,Gallagher Brian²,Chellappan Karthik³,Li Peggy³,Liu Shusen²,Kim Hyojin²,Han Jinkyu¹^ORCID,Kailkhura Bhavya²,Buttler David J.²,Han Thomas Yong-Jin¹^ORCID

Affiliation:

1. Materials Science Division, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, California 94550, United States

2. Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, California 94550, United States

3. Global Security Computing Applications Division, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, California 94550, United States

Funder

Lawrence Livermore National Laboratory

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.0c00199

Reference82 articles.

1. Materials Data Science: Current Status and Future Outlook

2. The Cambridge Structural Database: a quarter of a million crystal structures and rising

3. Crystallography Open Database – an open-access collection of crystal structures