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Materials Informatics for the Development and Discovery of Future Magnetic Materials

Journal Article · · IEEE Magnetics Letters
 [1];  [2];  [3];  [4];  [5];  [6];  [7];  [7];  [8]
  1. Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA; OSTI
  2. Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
  3. Center for Basic Research on Materials, National Institute for Materials Science, Tsukuba, Japan
  4. Laboratoire de Physique de l',Ecole Normale Supé,rieure, Université, PSL, CNRS, Sorbonne Université,, Paris, France
  5. Max-Planck-Institut fü,r Chemische Physik Fester Stoffe, Dresden, Germany
  6. Research Institute of Electrical Communication, Tohoku University, Sendai, Japan
  7. CD Labor for Magnet Design Through Physics Informed Machine Learning, Danube University Krems, Wiener Neustadt, Austria
  8. Center for Science and Innovation in Spintronics, Core Research Cluster, Tohoku University, Sendai, Japan
+ Show Author Affiliations
Not provided.
Research Organization:
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Sponsoring Organization:
USDOE Office of Science (SC)
DOE Contract Number:
SC0021940
OSTI ID:
2421966
Journal Information:
IEEE Magnetics Letters, Journal Name: IEEE Magnetics Letters Vol. 14; ISSN 1949-307X
Publisher:
IEEE
Country of Publication:
United States
Language:
English

References (14)

Machine learning autonomous identification of magnetic alloys beyond the Slater-Pauling limit journal March 2021
Detection of topological materials with machine learning journal June 2020
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation journal July 2013
Machine learning magnetism classifiers from atomic coordinates journal October 2022
Perspective and Prospects for Rare Earth Permanent Magnets journal February 2020
MaterialsAtlas.org: a materials informatics web app platform for materials discovery and survey of state-of-the-art journal April 2022
Physics-informed machine learning combining experiment and simulation for the design of neodymium-iron-boron permanent magnets with reduced critical-elements content journal January 2023
All topological bands of all nonmagnetic stoichiometric materials journal May 2022
A complete catalogue of high-quality topological materials journal February 2019
Heusler alloys for spintronic devices: review on recent development and future perspectives journal March 2021
Machine‐Learning Spectral Indicators of Topology journal October 2022
Boosting material modeling using game tree search journal October 2018
PLS regression methods journal June 1988
Research and Implementation of High Computational Power for Training and Inference of Convolutional Neural Networks journal January 2023

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