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      Disordered enthalpy–entropy descriptor for high-entropy ceramics discovery

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          Abstract

          The need for improved functionalities in extreme environments is fuelling interest in high-entropy ceramics 13 . Except for the computational discovery of high-entropy carbides, performed with the entropy-forming-ability descriptor 4 , most innovation has been slowly driven by experimental means 13 . Hence, advancement in the field needs more theoretical contributions. Here we introduce disordered enthalpy–entropy descriptor (DEED), a descriptor that captures the balance between entropy gains and enthalpy costs, allowing the correct classification of functional synthesizability of multicomponent ceramics, regardless of chemistry and structure. To make our calculations possible, we have developed a convolutional algorithm that drastically reduces computational resources. Moreover, DEED guides the experimental discovery of new single-phase high-entropy carbonitrides and borides. This work, integrated into the AFLOW computational ecosystem, provides an array of potential new candidates, ripe for experimental discoveries.

          Abstract

          DEED captures the balance between entropy gains and costs, allowing the correct classification of functional synthesizability of multicomponent ceramics, regardless of chemistry and structure, and provides an array of potential new candidates, ripe for experimental discoveries.

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          A critical review of high entropy alloys and related concepts

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            High-entropy alloys

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              Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification.

              State-of-the-art light and electron microscopes are capable of acquiring large image datasets, but quantitatively evaluating the data often involves manually annotating structures of interest. This process is time-consuming and often a major bottleneck in the evaluation pipeline. To overcome this problem, we have introduced the Trainable Weka Segmentation (TWS), a machine learning tool that leverages a limited number of manual annotations in order to train a classifier and segment the remaining data automatically. In addition, TWS can provide unsupervised segmentation learning schemes (clustering) and can be customized to employ user-designed image features or classifiers.
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                Author and article information

                Contributors
                stefano@duke.edu
                Journal
                Nature
                Nature
                Nature
                Nature Publishing Group UK (London )
                0028-0836
                1476-4687
                3 January 2024
                3 January 2024
                2024
                : 625
                : 7993
                : 66-73
                Affiliations
                [1 ]Department of Mechanical Engineering and Materials Science, Duke University, ( https://ror.org/00py81415) Durham, NC USA
                [2 ]Center for Autonomous Materials Design, Duke University, ( https://ror.org/00py81415) Durham, NC USA
                [3 ]Department of Materials Science and Engineering and Department of Chemistry and Biochemistry, The University of Texas at Dallas, ( https://ror.org/049emcs32) Richardson, TX USA
                [4 ]Institute of Ion Beam Physics and Materials Research, Helmholtz-Zentrum Dresden-Rossendorf, ( https://ror.org/01zy2cs03) Dresden, Germany
                [5 ]GRID grid.4488.0, ISNI 0000 0001 2111 7257, Theoretical Chemistry, , Technical University of Dresden, ; Dresden, Germany
                [6 ]Department of Physics, NRCN, Beer-Sheva, Israel
                [7 ]GRID grid.273335.3, ISNI 0000 0004 1936 9887, Department of Chemistry, , State University of New York at Buffalo, ; Buffalo, NY USA
                [8 ]Department of Materials Science and Engineering, The Pennsylvania State University, ( https://ror.org/04p491231) University Park, PA USA
                [9 ]Department of Materials Science and Engineering, North Carolina State University, ( https://ror.org/04tj63d06) Raleigh, NC USA
                [10 ]Department of Materials Science and Engineering, Missouri University of Science and Technology, ( https://ror.org/00scwqd12) Rolla, MO USA
                [11 ]Institute of Technical Sciences of the Serbian Academy of Sciences and Arts, ( https://ror.org/02f2wk572) Belgrade, Serbia
                [12 ]GRID grid.29857.31, ISNI 0000 0001 2097 4281, Applied Research Laboratory, , The Pennsylvania State University, ; University Park, PA USA
                [13 ]GRID grid.421737.4, ISNI 0000 0004 1768 9932, CNR-NANO Research Center S3, ; Modena, Italy
                Author information
                http://orcid.org/0000-0002-4185-6150
                http://orcid.org/0000-0003-4771-1435
                http://orcid.org/0000-0002-4066-3840
                http://orcid.org/0000-0003-0738-867X
                http://orcid.org/0000-0001-6383-8327
                http://orcid.org/0000-0002-8497-0092
                http://orcid.org/0000-0002-3119-857X
                http://orcid.org/0009-0002-7705-6005
                http://orcid.org/0000-0002-0244-7717
                http://orcid.org/0000-0003-0570-8238
                Article
                6786
                10.1038/s41586-023-06786-y
                10764291
                38172364
                7a1b238e-09af-4a56-a73b-74497413cd07
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 31 March 2023
                : 26 October 2023
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                © Springer Nature Limited 2024

                Uncategorized
                ceramics,coarse-grained models
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                ceramics, coarse-grained models

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