Inverse Design of Materials That Exhibit the Magnetocaloric Effect by Text-Mining of the Scientific Literature and Generative Deep Learning-Reference-Cited by-同舟云学术

Inverse Design of Materials That Exhibit the Magnetocaloric Effect by Text-Mining of the Scientific Literature and Generative Deep Learning

Published:2021-09-16 Issue:18 Volume:33 Page:7217-7231
ISSN:0897-4756
Container-title:Chemistry of Materials
language:en
Short-container-title:Chem. Mater.

Author:

Court Callum J.¹^ORCID,Jain Apoorv¹²,Cole Jacqueline M.¹²³^ORCID

Affiliation:

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

2. Department of Chemical Engineering and Biotechnology, University of Cambridge, West Cambridge Site, Philippa Fawcett Drive, Cambridge CB3 0FS, U.K.

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

Funder

Royal Academy of Engineering

BASF

Science and Technology Facilities Council

Publisher

American Chemical Society (ACS)

Subject

Materials Chemistry,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.chemmater.1c01368

Reference47 articles.

1. Coey, J. M. D. Magnetism and Magnetic Materials; Cambridge University Press, 2010; pp 374–432.

2. Blundell, S. Magnetism in Condensed Matter; Oxford University Press, 2003; pp 74–82.

3. Coronado, E.; Tsukerblat, B. S.; Georges, R. In Molecular Magnetism: From Molecular Assemblies to the Devices; Coronado, E., Delhaès, P., Gatteschi, D., Miller, J. S., Eds. Springer Netherlands: Dordrecht, The Netherlands, 1996; pp 65–84.

4. Database of novel magnetic materials for high-performance permanent magnet development

5. Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

Cited by 30 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Accurate prediction of magnetocaloric effect in NiMn-based Heusler alloys by prioritizing phase transitions through explainable machine learning;Rare Metals;2024-08-29

2. Machine-Learning Prediction of Curie Temperature from Chemical Compositions of Ferromagnetic Materials;Journal of Chemical Information and Modeling;2024-08-07

3. Accelerating Materials Discovery for Polymer Solar Cells: Data-Driven Insights Enabled by Natural Language Processing;Chemistry of Materials;2024-08-06

4. Synthesis, Characterization, and Magnetocaloric Properties of the Ternary Boride Fe2AlB2 for Caloric Applications;Materials;2024-08-06

5. Enhancing magnetocaloric material discovery: A machine learning approach using an autogenerated database by large language models;AIP Advances;2024-08-01