ReactionDataExtractor 2.0: A Deep Learning Approach for Data Extraction from Chemical Reaction Schemes-Reference-Cited by-同舟云学术

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ReactionDataExtractor 2.0: A Deep Learning Approach for Data Extraction from Chemical Reaction Schemes

Published:2023-09-20 Issue:19 Volume:63 Page:6053-6067
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Wilary Damian M.¹,Cole Jacqueline M.¹²^ORCID

Affiliation:

1. Cavendish Laboratory, Department of Physics, University of Cambridge, J. J. Thomson Avenue, Cambridge, CB3 0HE, U.K.

2. ISIS Neutron and Muon Source, STFC Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0QX, U.K.

Funder

Royal Academy of Engineering

Science and Technology Facilities Council

BASF Corporation

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.3c00422

Reference46 articles.

1. The Harvard Clean Energy Project: Large-Scale Computational Screening and Design of Organic Photovoltaics on the World Community Grid

2. NOMAD: The FAIR concept for big data-driven materials science

3. ChemDataExtractor: A Toolkit for Automated Extraction of Chemical Information from the Scientific Literature

4. Auto-generated materials database of Curie and Néel temperatures via semi-supervised relationship extraction

5. ChemDataExtractor 2.0: Autopopulated Ontologies for Materials Science

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