Efficient global crystal structure prediction using polynomial machine learning potential in the binary Al–Cu alloy system
Machine learning potentials (MLPs) are attracting much attention as powerful tools to accurately and efficiently perform atomistic simulations and crystal structure predictions. In this study, we develop a polynomial MLP for the Al–Cu system applicable to the robust global structure search and metas...
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| Veröffentlicht in: | Journal of the Ceramic Society of Japan 2023/10/01, Vol.131(10), pp.762-766 |
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| container_title | Journal of the Ceramic Society of Japan |
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| creator | Wakai, Hayato Seko, Atsuto Tanaka, Isao |
| description | Machine learning potentials (MLPs) are attracting much attention as powerful tools to accurately and efficiently perform atomistic simulations and crystal structure predictions. In this study, we develop a polynomial MLP for the Al–Cu system applicable to the robust global structure search and metastable structure enumeration. We then apply a combination of a global optimization method and the polynomial MLP to the Al–Cu alloy system. As a result of approximately 1010 times energy computations, the globally-stable and metastable structures are enumerated in the Al–Cu system. |
| doi_str_mv | 10.2109/jcersj2.23053 |
| format | Article |
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As a result of approximately 1010 times energy computations, the globally-stable and metastable structures are enumerated in the Al–Cu system.</description><subject>Alloy system</subject><subject>Alloy systems</subject><subject>Aluminum base alloys</subject><subject>Binary alloys</subject><subject>Crystal structure</subject><subject>Crystal structure prediction</subject><subject>Density functional theory (DFT) calculation</subject><subject>Enumeration</subject><subject>Global optimization</subject><subject>Machine learning</subject><subject>Machine learning potentials</subject><subject>Polynomials</subject><issn>1882-0743</issn><issn>1348-6535</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNo9kLFOwzAQhiMEEqUwsltiTontxLHHqmoBqRILzJFjO60j1wm2M0Ri4B14Q54Et6k6_Sfdd__d_UnyCLMFghl7boVyvkULhLMCXyUziHOakgIX17GmFKVZmePb5M77NssIyjGdJd_rptFCKxvAznQ1N0C40YeoPrhBhMEp0DsltQi6s2Dw2u5A35nRdgcdqQMXe20VMIo7O_VCNDu2tAVhr0CtLXcjWJq_n9_VALgx3Qh83KEO98lNw41XD2edJ5-b9cfqNd2-v7ytlttUIAZx2hDCeCmhFKIuqKREQColEZzKsoRMEkIkozUjTDFYZoLLnNciz_P4IkSE43nyNPn2rvsalA9V2w3OxpUVopRRWpQFilQ6UcJ13jvVVL3Th3h7BbPqGHB1Drg6BRz5zcS3Ma-dutDcBS2MutAQw6PDUU6DF0DsuauUxf9G8IwM</recordid><startdate>20231001</startdate><enddate>20231001</enddate><creator>Wakai, Hayato</creator><creator>Seko, Atsuto</creator><creator>Tanaka, Isao</creator><general>The Ceramic Society of Japan</general><general>Japan Science and Technology Agency</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QQ</scope><scope>7SR</scope><scope>8FD</scope><scope>JG9</scope></search><sort><creationdate>20231001</creationdate><title>Efficient global crystal structure prediction using polynomial machine learning potential in the binary Al–Cu alloy system</title><author>Wakai, Hayato ; Seko, Atsuto ; Tanaka, Isao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2913-f669a7d1dccb58d86c18dd6ca8d7719d666d98b969e9170cad4abc444243126a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng ; jpn</language><creationdate>2023</creationdate><topic>Alloy system</topic><topic>Alloy systems</topic><topic>Aluminum base alloys</topic><topic>Binary alloys</topic><topic>Crystal structure</topic><topic>Crystal structure prediction</topic><topic>Density functional theory (DFT) calculation</topic><topic>Enumeration</topic><topic>Global optimization</topic><topic>Machine learning</topic><topic>Machine learning potentials</topic><topic>Polynomials</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wakai, Hayato</creatorcontrib><creatorcontrib>Seko, Atsuto</creatorcontrib><creatorcontrib>Tanaka, Isao</creatorcontrib><collection>CrossRef</collection><collection>Ceramic Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><jtitle>Journal of the Ceramic Society of Japan</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wakai, Hayato</au><au>Seko, Atsuto</au><au>Tanaka, Isao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Efficient global crystal structure prediction using polynomial machine learning potential in the binary Al–Cu alloy system</atitle><jtitle>Journal of the Ceramic Society of Japan</jtitle><addtitle>J. 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| ispartof | Journal of the Ceramic Society of Japan, 2023/10/01, Vol.131(10), pp.762-766 |
| issn | 1882-0743 1348-6535 |
| language | eng ; jpn |
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| source | J-STAGE Free |
| subjects | Alloy system Alloy systems Aluminum base alloys Binary alloys Crystal structure Crystal structure prediction Density functional theory (DFT) calculation Enumeration Global optimization Machine learning Machine learning potentials Polynomials |
| title | Efficient global crystal structure prediction using polynomial machine learning potential in the binary Al–Cu alloy system |
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