Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks-Reference-Cited by-同舟云学术

Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks

Published:2020-01-07 Issue:3 Volume:60 Page:1194-1201
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Kim Edward¹^ORCID,Jensen Zach¹^ORCID,van Grootel Alexander¹,Huang Kevin¹,Staib Matthew²,Mysore Sheshera³,Chang Haw-Shiuan³,Strubell Emma³,McCallum Andrew³,Jegelka Stefanie²,Olivetti Elsa¹^ORCID

Affiliation:

1. Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States

2. Department of EECS and CSAIL, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States

3. College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States

Funder

Directorate for Engineering

Basic Energy Sciences

Office of Naval Research Global

MIT Energy Initiative

MIT-Sensetime Alliance on Artificial Intelligence

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.9b00995

Reference40 articles.

1. Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

2. Accelerated materials property predictions and design using motif-based fingerprints

3. Materials Cartography: Representing and Mining Materials Space Using Structural and Electronic Fingerprints

4. Combinatorial screening for new materials in unconstrained composition space with machine learning

5. Network analysis of synthesizable materials discovery

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