{"id":5847,"date":"2025-11-25T16:42:43","date_gmt":"2025-11-25T07:42:43","guid":{"rendered":"https:\/\/matlantis.com\/ja\/?post_type=event_seminar&p=5847"},"modified":"2026-01-13T13:29:23","modified_gmt":"2026-01-13T04:29:23","slug":"2025-mrs-fall-meeting-exhibit","status":"publish","type":"event_seminar","link":"https:\/\/matlantis.com\/ja\/resources\/event-seminar\/2025-mrs-fall-meeting-exhibit\/","title":{"rendered":"2025 MRS Fall Meeting & Exhibit\u767a\u8868\u304a\u3088\u3073\u51fa\u5c55\u306e\u304a\u77e5\u3089\u305b"},"content":{"rendered":"\n

Matlantis\u682a\u5f0f\u4f1a\u793e\u306f2025\u5e7411\u670830\u65e5\uff5e12\u67085\u65e5\uff08\u30a2\u30e1\u30ea\u30ab\u6771\u90e8\u6a19\u6e96\u6642\uff09\u306b\u958b\u50ac\u3055\u308c\u308b\u300c2025 MRS Fall Meeting & Exhibit\u300d\u306b\u51fa\u5c55\u3044\u305f\u3057\u307e\u3059\u3002Matlantis\u304b\u3089\u306fAPAC\u30ab\u30b9\u30bf\u30de\u30fc\u30b5\u30af\u30bb\u30b9\u30c1\u30fc\u30e0\u30ea\u30fc\u30c0\u30fc\u306e\u540d\u5150\u8036\u304c\u53e3\u982d\u767a\u8868\u3092\u884c\u3046\u4ed6\u3001\u30d6\u30fc\u30b9\u51fa\u5c55\u3082\u3044\u305f\u3057\u307e\u3059\u3002\u307e\u305f\u3053\u306e\u4ed6\u306b\u3082\u793e\u5916\u306e\u7d44\u7e54\u304b\u3089Matlantis\u306b\u95a2\u9023\u3057\u3066\u767a\u8868\u304c\u3054\u3056\u3044\u307e\u3059\u3002\u5185\u5bb9\u306f\u4ee5\u4e0b\u3054\u53c2\u7167\u304f\u3060\u3055\u3044\u3002<\/p>\n\n\n\n

\u30d6\u30fc\u30b9\u51fa\u5c55\u60c5\u5831<\/h2>\n\n\n\n

\u5c55\u793a\u671f\u9593<\/strong> : 2025\u5e7412\u67082\u65e5\uff5e12\u67084\u65e5\uff08\u30a2\u30e1\u30ea\u30ab\u6771\u90e8\u6a19\u6e96\u6642\uff09
\u958b\u50ac\u5834\u6240<\/strong> : Exhibit Hall C, Level 2 at the Hynes Convention Center
\u30d6\u30fc\u30b9<\/strong> : 602<\/p>\n\n\n\n

\u30d7\u30ec\u30bc\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u8a73\u7d30<\/h2>\n\n\n\n

\u767a\u8868\u30bf\u30a4\u30c8\u30eb:<\/strong> Atomistic Simulations of Metals and Alloys Using Universal Graph Neural Network Interatomic Potentials<\/p>\n\n\n\n

\u65e5\u6642:<\/strong> 2025\u5e7412\u67083\u65e5 11:30 AM \u2013 11:45 AM\uff08\u30a2\u30e1\u30ea\u30ab\u6771\u90e8\u6a19\u6e96\u6642\uff09
\u90e8\u9580:<\/strong> MT04.06 Metals and Alloys I
\u30eb\u30fc\u30e0<\/strong>: Hynes\u201a Level 3\u201a Room 312
\u30bb\u30c3\u30b7\u30e7\u30f3\u30bf\u30a4\u30d7:<\/strong> \u53e3\u982d
\u767a\u8868\u8005:<\/strong> Akihiro Nagoya
\u8457\u8005:<\/strong> Akihiro Nagoya, Taku Watanabe
\u6982\u8981: <\/strong>Atomistic simulations are essential for the research and development of metallic materials, providing atomic-level insights into interfaces, defects, and complex microstructures to support alloy design and process optimization. While Molecular Dynamics (MD) is widely used, classical force fields lack the necessary accuracy and transferability for chemically complex or non-equilibrium systems, and high-accuracy Density Functional Theory (DFT) is computationally prohibitive for large-scale MD simulations.<\/p>\n\n\n\n

Machine learning interatomic potentials (MLIPs) have emerged as a powerful method offering near-DFT accuracy at a drastically reduced computational cost. The PreFerred Potential (PFP) is a graph neural network-based MLIP trained on a huge DFT dataset. Owing to its universality and robustness, PFP has been applied to various systems, including semiconductors, battery materials, catalysts, metal\u2013organic frameworks, and metals and alloys. To enhance its efficiency, we recently developed LightPFP, a moment tensor potential trained on the PFP dataset. LightPFP is ten times faster than the original PFP, striking an optimal balance between accuracy and speed, which makes it ideal for large-scale Molecular Dynamics (MD) simulations of metallic systems.<\/p>\n\n\n\n

In this study, we validated the accuracy of PFP by calculating thermodynamic properties of several simple metals, showing good agreement with DFT results reported in the literature. Then, we demonstrate the applicability of both PFP and LightPFP to MD simulations of mechanical and interfacial phenomena. These results highlight the universality and robustness of our MLIPs, establishing them as powerful tools for accelerating metals and alloys developments.<\/p>\n\n\n\n

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\u540d\u5150\u8036 \u5f70\u6d0b
<\/strong>Matlantis\u682a\u5f0f\u4f1a\u793e APAC\u30ab\u30b9\u30bf\u30de\u30fc\u30b5\u30af\u30bb\u30b9\u30c1\u30fc\u30e0\u30ea\u30fc\u30c0\u30fc\u3002
\u5927\u962a\u5927\u5b66\u3092\u5352\u696d\u5f8c\u306b\u682a\u5f0f\u4f1a\u793e\u8c4a\u7530\u4e2d\u592e\u7814\u7a76\u6240\u306e\u7814\u7a76\u54e1\u3068\u3057\u3066\u52e4\u52d9\u3002\u592a\u967d\u96fb\u6c60\u6750\u6599\u3001\u71c3\u6599\u96fb\u6c60\u767d\u91d1\u89e6\u5a92\u3001\u4e8c\u6b21\u5143\u6750\u6599\u306e\u7b2c\u4e00\u539f\u7406\u8a08\u7b97\u3084\u9ad8\u5206\u5b50\u306e\u53e4\u5178MD\u8a08\u7b97\u3092\u4f7f\u7528\u3057\u305fMI\u306a\u3069\u306e\u7814\u7a76\u306b\u7d0415\u5e74\u9593\u5f93\u4e8b\u3057\u305f\u3002\u305d\u306e\u5f8c\u3001ENEOS\u682a\u5f0f\u4f1a\u793e\u306e\u4e2d\u592e\u6280\u8853\u7814\u7a76\u6240\u3092\u7d4c\u3066Matlantis\u306b\u5165\u793e\u3002\u73fe\u5728\u306f\u4e3b\u306b\u96fb\u6c60\u6750\u6599\u3084\u91d1\u5c5e\u6750\u6599\u306b\u95a2\u308f\u308b\u8a08\u7b97\u306b\u643a\u308f\u3063\u3066\u3044\u308b\u3002<\/p>\n<\/div>\n\n\n\n

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\"\"<\/figure>\n<\/div>\n<\/div>\n\n\n\n

Matlantis\u306b\u95a2\u3059\u308b\u793e\u5916\u306e\u8b1b\u6f14<\/h2>\n\n\n\n

2025\u5e7412\u67081\u65e5<\/strong><\/p>\n\n\n\n

\u767a\u8868\u30bf\u30a4\u30c8\u30eb:<\/strong> How Reliable Are Machine Learning Potentials? An Assessment of Uncertainty Estimation Methods in LightPFP
\u65e5\u6642:<\/strong> 2025\u5e7412\u67081\u65e5 11:45 AM \u2013 12:00 PM\uff08\u30a2\u30e1\u30ea\u30ab\u6771\u90e8\u6a19\u6e96\u6642\uff09
\u90e8\u9580:<\/strong> MT04.01 Machine Learning Potentials I
\u30eb\u30fc\u30e0:<\/strong> Hynes\u201a Level 3\u201a Room 312
\u30bb\u30c3\u30b7\u30e7\u30f3\u30bf\u30a4\u30d7:<\/strong> \u53e3\u982d
\u767a\u8868\u8005:<\/strong> Nontawat Charoenphakdee
\u8457\u8005:<\/strong> Nontawat Charoenphakdee, Wenwen Li, Yuta Tsuboi, Junichi Ishida, Ju Li
\u7d44\u7e54:<\/strong> Preferred Networks, inc., Matlantis Corporation, Massachusetts Institute of Technology<\/p>\n\n\n\n

2025\u5e7412\u67082\u65e5<\/strong><\/p>\n\n\n\n

\u767a\u8868\u30bf\u30a4\u30c8\u30eb:<\/strong> LLM Agent System for Autonomous Generation of 3D Atomic Structures from Textual Descriptions
\u65e5\u6642:<\/strong> 2025\u5e7412\u67082\u65e5 15:30 PM \u2013 15:45 PM\uff08\u30a2\u30e1\u30ea\u30ab\u6771\u90e8\u6a19\u6e96\u6642\uff09
\u90e8\u9580:<\/strong> MT06.04 Generative AI Meets Materials Modeling\u2014Emerging Opportunities and Challenges IVI
\u30eb\u30fc\u30e0:<\/strong> Hynes\u201a Level 3\u201a Room 309
\u30bb\u30c3\u30b7\u30e7\u30f3\u30bf\u30a4\u30d7:<\/strong> \u53e3\u982d
\u767a\u8868\u8005:<\/strong> Iori Kurata
\u8457\u8005:<\/strong> Iori Kurata, Ryohto Sawada, Terumasa Shimada, Yuki Orimo, Hodaka Mori
\u7d44\u7e54:<\/strong> Preferred Networks, Inc., ENEOS Holdings, Inc.<\/p>\n\n\n\n

\u767a\u8868\u30bf\u30a4\u30c8\u30eb:<\/strong> Development of r2SCAN Level Universal Neural Network Potential and Its Applications
\u65e5\u6642:<\/strong> 2025\u5e7412\u67082\u65e5 19:00 PM \u2013 21:00 PM\uff08\u30a2\u30e1\u30ea\u30ab\u6771\u90e8\u6a19\u6e96\u6642\uff09
\u90e8\u9580:<\/strong> MT04.05 Poster Session I: Integrating Machine Learning and Simulations for Materials Modeling I
\u30eb\u30fc\u30e0:<\/strong> Hynes\u201a Level 1\u201a Hall A
\u30bb\u30c3\u30b7\u30e7\u30f3\u30bf\u30a4\u30d7:<\/strong> \u30dd\u30b9\u30bf\u30fc
\u767a\u8868\u8005:<\/strong> So Takamoto
\u8457\u8005:<\/strong> So Takamoto, Chikashi Shinagawa, Daiki Shintani, Katsuhiko Nishimra, Kohei Shinohara, Shigeru Iwase, Ju Li
\u7d44\u7e54:<\/strong> Preferred Networks, Inc., Massachusetts Institute of Technology<\/p>\n\n\n\n

2025\u5e7412\u67084\u65e5<\/strong><\/p>\n\n\n\n

\u767a\u8868\u30bf\u30a4\u30c8\u30eb:<\/strong> Enhancing Crystal Structure Prediction with r2SCAN-Level Universal Neural Network Potentials
\u65e5\u6642:<\/strong> 2025\u5e7412\u67084\u65e5 19:00 PM \u2013 21:00 PM\uff08\u30a2\u30e1\u30ea\u30ab\u6771\u90e8\u6a19\u6e96\u6642\uff09
\u90e8\u9580:<\/strong> MT03.09 Poster Session: Accelerated Materials Discovery Through Data-Driven AI and Automation
\u30eb\u30fc\u30e0:<\/strong> Hynes\u201a Level 1\u201a Hall A
\u30bb\u30c3\u30b7\u30e7\u30f3\u30bf\u30a4\u30d7:<\/strong> \u30dd\u30b9\u30bf\u30fc
\u767a\u8868\u8005:<\/strong> Kohei Shinohara
\u8457\u8005:<\/strong> Kohei Shinohara, Hideaki Imamura, Katsuhiko Nishimra, Shuhei Watanabe, Kaoru Hisama, Chikashi Shinagawa, So Takamoto, Ju Li
\u7d44\u7e54:<\/strong> Preferred Networks, Inc., Massachusetts Institute of Technology<\/p>\n\n\n\n

\u5b66\u4f1a\u516c\u5f0f\u60c5\u5831\u306f\u3053\u3061\u3089 (\u5916\u90e8\u30b5\u30a4\u30c8) >> 2025 MRS Fall Meeting & Exhibit<\/a><\/p>\n","protected":false},"featured_media":4698,"template":"","meta":{"_acf_changed":true},"event_seminar_article_category":[72,71],"event_seminar_type_category":[78],"class_list":["post-5847","event_seminar","type-event_seminar","status-publish","has-post-thumbnail","hentry","event_seminar_article_category-after","event_seminar_article_category-hold","event_seminar_type_category-conferences_lectures"],"acf":[],"_links":{"self":[{"href":"https:\/\/matlantis.com\/ja\/wp-json\/wp\/v2\/event_seminar\/5847","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/matlantis.com\/ja\/wp-json\/wp\/v2\/event_seminar"}],"about":[{"href":"https:\/\/matlantis.com\/ja\/wp-json\/wp\/v2\/types\/event_seminar"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/matlantis.com\/ja\/wp-json\/wp\/v2\/media\/4698"}],"wp:attachment":[{"href":"https:\/\/matlantis.com\/ja\/wp-json\/wp\/v2\/media?parent=5847"}],"wp:term":[{"taxonomy":"event_seminar_article_category","embeddable":true,"href":"https:\/\/matlantis.com\/ja\/wp-json\/wp\/v2\/event_seminar_article_category?post=5847"},{"taxonomy":"event_seminar_type_category","embeddable":true,"href":"https:\/\/matlantis.com\/ja\/wp-json\/wp\/v2\/event_seminar_type_category?post=5847"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}