Magnetic and superconducting phase diagrams and transition temperatures predicted using text mining and machine learning
Published version
Peer-reviewed
Repository URI
Repository DOI
Type
Change log
Authors
Abstract
jats:titleAbstract</jats:title>jats:pPredicting the properties of materials prior to their synthesis is of great importance in materials science. Magnetic and superconducting materials exhibit a number of unique properties that make them useful in a wide variety of applications, including solid oxide fuel cells, solid-state refrigerants, photon detectors and metrology devices. In all these applications, phase transitions play an important role in determining the feasibility of the materials in question. Here, we present a pipeline for fully integrating data extracted from the scientific literature into machine-learning tools for property prediction and materials discovery. Using advanced natural language processing (NLP) and machine-learning techniques, we successfully reconstruct the phase diagrams of well-known magnetic and superconducting compounds, and demonstrate that it is possible to predict the phase-transition temperatures of compounds not present in the database. We provide the tool as an online open-source platform, forming the basis for further research into magnetic and superconducting materials discovery for potential device applications.</jats:p>
Description
Keywords
Journal Title
Conference Name
Journal ISSN
2057-3960
Volume Title
Publisher
Publisher DOI
Sponsorship
Royal Academy of Engineering (RAEng) (RCSRF1819\7\10)
Engineering and Physical Sciences Research Council (EP/L015552/1)