MRS Communications
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ARTIFICIAL INTELLIGENCE SPECIAL ISSUE: RESEARCH LETTERS A picture is worth a thousand words: Applying natural language processing tools for creating a quantum materials database map Vineeth Venugopal, Scott R. Broderick, Krishna Rajan, University at Buffalo, The State University of New York, USA The authors demonstrate the application of natural language processing (NLP) tools to explore large libraries of documents and to correlate heuristic associations between text descriptions in figure captions with interpretations of images and figures. The use of visualization tools based on NLP methods permits one to quickly assess the extent of the research described in the literature related to a specific topic. The authors demonstrate how the use of NLP methods on only the figure captions without having to navigate the entire text of a document can provide an accelerated assessment of the literature in a given domain. doi.org/10.1557/mrc.2019.136
A data ecosystem to support machine learning in materials science Ben Blaiszik, Logan Ward, The University of Chicago, and Argonne National Laboratory, USA; Marcus Schwarting, Argonne National Laboratory, USA; Jonathon Gaff, The University of Chicago, USA; Ryan Chard, The University of Chicago, and Argonne National Laboratory, USA; Daniel Pike, Cornell University; Kyle Chard, Ian Foster, The University of Chicago, and Argonne National Laboratory, USA Facilitating the application of machine learning (ML) to materials science problems requires enhancing the data ecosystem to enable discovery and collection of data from many sources, automated dissemination of new data across the ecosystem, and the connecting of data with materials-specific ML models. Here, we present two projects, the Materials Data Facility (MDF) and the Data and Learning Hub for Science (DLHub), that address these needs. We use examples to show how MDF and DLHub capabilities can be leveraged to link data with ML models and how users can access those capabilities through web and programmatic interfaces. doi.org/10.1557/mrc.2019.118
PROSPECTIVES Theory and simulations of critical temperatures in CrI3 and other 2D materials: Easy-axis magnetic order and easyplane Kosterlitz–Thouless transitions Thomas Olsen, Technical University of Denmark, Denmark The recent observations of ferromagnetic order in several twodimensional (2D) materials have generated an enormous interest in the physical mechanisms underlying 2D magnetism. In the present prospective prticle, the author shows that density functional theory combined with either classical Monte Carlo simulations or renormalized spin-wave theory can predict Curie temperatures for ferromagnetic insulators that are in quantitative agreement with experiments. The case of materials with in-plane anisotropy is then discussed, and it is
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argued that finite size effects may lead to observable magnetic order in macroscopic samples even if long-range magnetic order is forbidden by the Mermin–Wagner theorem. doi.org/10.1557/mrc.2019.117
Redox-active polymers (redoxmers) for e
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