{"id":3066,"date":"2024-11-26T22:03:04","date_gmt":"2024-11-26T13:03:04","guid":{"rendered":"https:\/\/matlantis.com\/?post_type=news&p=3066"},"modified":"2025-05-30T19:10:27","modified_gmt":"2025-05-30T10:10:27","slug":"2024-mrs-fall-meeting-exhibit","status":"publish","type":"event_seminar","link":"https:\/\/matlantis.com\/ja\/resources\/event-seminar\/2024-mrs-fall-meeting-exhibit\/","title":{"rendered":"2024 MRS Fall Meeting & Exhibit \u767a\u8868\u304a\u3088\u3073\u51fa\u5c55\u306e\u304a\u77e5\u3089\u305b"},"content":{"rendered":"\n

PFCC\u306f 2024\u5e7412\u67081\u65e5\uff08\u65e5\uff09\uff5e12\u67086\u65e5\uff08\u91d1\uff09 \u306b\u958b\u50ac\u3055\u308c\u308b\u300c2024 MRS Fall Meeting & Exhibit\u300d\u306b\u51fa\u5c55\u3057\u307e\u3059\u3002PFCC\u304b\u3089\u306f3\u540d\u306e\u30e1\u30f3\u30d0\u30fc\u304c\u30dd\u30b9\u30bf\u30fc\u767a\u8868\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\u30dd\u30b9\u30bf\u30fc\u767a\u8868\u304c3\u4ef6\u3001\u53e3\u982d\u767a\u8868\u304c7\u4ef6\u3054\u3056\u3044\u307e\u3059\u3002\u5185\u5bb9\u306f\u3001\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> : 2024\u5e7412\u67083\u65e5\uff08\u706b\uff09\uff5e 12\u67085\u65e5\uff08\u6728\uff09
\u958b\u50ac\u5834\u6240<\/strong> : Hynes Convention Center, Boston, Massachusetts
\u30d6\u30fc\u30b9<\/strong> : 419<\/p>\n\n\n\n

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

\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0<\/strong>: MT04: Next-Generation AI-Catalyzed Scientific Workflow for Digital Materials Discovery
\u30eb\u30fc\u30e0<\/strong>: Hynes, Level 1, Hall A<\/p>\n\n\n\n

\u767a\u8868\u8005 1: \u6e21\u908a \u5353<\/h3>\n\n\n\n

\u30bb\u30c3\u30b7\u30e7\u30f3:<\/strong> MT04.05.11
\u65e5\u6642:<\/strong> 2024\u5e7412\u67083\u65e5 8:00 PM – 10:00 PM\uff08\u30a2\u30e1\u30ea\u30ab\u6771\u90e8\u6a19\u6e96\u6642\uff09 <\/strong> <\/strong>
\u30bb\u30c3\u30b7\u30e7\u30f3\u30bf\u30a4\u30d7:<\/strong> \u30dd\u30b9\u30bf\u30fc\u30d7\u30ec\u30bc\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3
\u30bf\u30a4\u30c8\u30eb<\/strong>: The Surface Configurations and Their Impact on Pd-Based Alloy Membranes for Hydrogen Separation\u2014An Application of a Universal Neural Network Potential
\u6982\u8981: <\/strong>Pd-based alloy membranes for hydrogen separation offer high efficiency and important practical advantages over traditional methods like pressure swing adsorption or cryogenic distillation. However, the rarity and expense of Pd, along with its reactivity with certain gas mixture components, pose significant challenges. Our study uses the Preferred Potential (PFP) neural network to perform atomistic simulations that explore the impact of alloy configurations on hydrogen adsorption and diffusion at the surface. PFP allows near-DFT accuracy in simulating chemical reactions on larger systems and at higher speeds.
Our findings using Monte Carlo simulations indicate pronounced surface segregation behaviors in fcc Pd-based alloys, with specific patterns for Pd3Ag, Pd3Au, Pd3Cu, Pd3Ni, and Pd3Pt. These results align with known literature. Additionally, ternary phases like Au-Ag, Au-Cu, and Cu-Ag in Pd reveal similar segregation trends and interesting subsurface structures. The variation in surface composition significantly affects hydrogen binding energies and migration energy barriers, suggesting that the choice of alloy composition could optimize membrane performance for hydrogen separation.<\/p>\n\n\n\n

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\u6e21\u908a \u5353<\/strong>
\u30d5\u30ed\u30ea\u30c0\u5927\u5b66\u3067Materials Science and Engineering\u306e\u535a\u58eb\u53f7\u3092\u53d6\u5f97\u5f8c\u3001\u30b8\u30e7\u30fc\u30b8\u30a2\u5de5\u79d1\u5927\u5b66\u3067\u5316\u5b66\u5de5\u5b66\u306e\u7814\u7a76\u54e1\u3068\u3057\u3066\u52e4\u52d9\u3002
2012\u5e74\u306b\u682a\u5f0f\u4f1a\u793e\u30b5\u30e0\u30b9\u30f3\u65e5\u672c\u7814\u7a76\u6240\u306b\u5165\u793e\u3057\u3001\u5168\u56fa\u4f53\u96fb\u6c60\u306e\u7814\u7a76\u306b\u7d048\u5e74\u9593\u5f93\u4e8b\u3002
\u73fe\u5728\u306f\u3001\u30d0\u30c3\u30c6\u30ea\u30fc\u6750\u6599\u3001\u30ca\u30ce\u30dd\u30fc\u30e9\u30b9\u56fa\u4f53\u3001\u8868\u9762\u79d1\u5b66\u3001\u305d\u3057\u3066\u8a08\u7b97\u5316\u5b66\u306b\u304a\u3051\u308b\u6a5f\u68b0\u5b66\u7fd2\u6280\u8853\u306e\u5fdc\u7528\u3068\u3044\u3063\u305f\u7814\u7a76\u306b\u53d6\u308a\u7d44\u3080\u3002<\/p>\n<\/div>\n\n\n\n

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

\u767a\u8868\u8005 2: \u540d\u5150\u8036 \u5f70\u6d0b<\/h3>\n\n\n\n

\u30bb\u30c3\u30b7\u30e7\u30f3:<\/strong> MT04.05.17
\u65e5\u6642:<\/strong> 2024\u5e7412\u67083\u65e5 8:00 PM – 10:00 PM\uff08\u30a2\u30e1\u30ea\u30ab\u6771\u90e8\u6a19\u6e96\u6642\uff09 <\/strong>
\u30bb\u30c3\u30b7\u30e7\u30f3\u30bf\u30a4\u30d7: <\/strong>\u30dd\u30b9\u30bf\u30fc\u30d7\u30ec\u30bc\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3
\u30bf\u30a4\u30c8\u30eb: <\/strong>Evaluation of a Universal Neural Network Potential for Predicting Finite Temperature Properties Using Quasi-Harmonic Approximation
\u6982\u8981:<\/strong> Predicting material properties at finite temperature requires accurate evaluation of thermodynamic quantities such as Gibbs free energy. The Preferred Potential (PFP) implemented on MatlantisTM<\/sup> is a recently developed graph neural network potential with the unique feature of universality[1]. PFP is trained on large DFT data sets, including not only stable crystals and molecules, but also surfaces and disordered structures. As a result, it is applicable to predict finite temperature properties of materials without compromising accuracy.
In this study, we have systematically validated the accuracy of PFP for predicting thermodynamics properties at finite temperature. The temperature dependence of the specific heats around room temperature shows good agreement with experiments. The miscibility gap of MgO-CaO was qualitatively predicted using semi-Grand Canonical Monte Carlo (sGCMC) simulations. These results demonstrate the accuracy and universality of PFP which is applicable to predict material properties at finite temperature.<\/p>\n\n\n\n

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\u540d\u5150\u8036 \u5f70\u6d0b<\/strong>
\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\u3066PFCC\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

\u767a\u8868\u8005 3: \u677e\u672c \u7693\u592a<\/h3>\n\n\n\n

\u30bb\u30c3\u30b7\u30e7\u30f3:<\/strong> MT04.09.10
\u65e5\u6642:<\/strong> 2024\u5e7412\u67084\u65e5 8:00 PM – 10:00 PM\uff08\u30a2\u30e1\u30ea\u30ab\u6771\u90e8\u6a19\u6e96\u6642\uff09
\u30bb\u30c3\u30b7\u30e7\u30f3\u30bf\u30a4\u30d7:<\/strong>\u00a0\u30dd\u30b9\u30bf\u30fc\u30d7\u30ec\u30bc\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3
\u30bf\u30a4\u30c8\u30eb:<\/strong> Wet Hydrofluoric Acid Etching Reaction Mechanism Analysis of Silicon Oxide Using GRRM with Universal Neural Network Potential
\u6982\u8981: <\/strong>In semiconductor manufacturing technology that is advancing towards miniaturization, the improvement of wet etching process reactions at the atomic level, which utilizes dilute hydrofluoric (HF) acid solution to remove oxides from substrate surfaces, has become increasingly important. Although there has been active research aimed at understanding at the atomic level, it has been difficult to handle the formation and cleavage of bonds in molecular dynamics simulations, and due to the high computational cost, first-principle calculations have struggled with the analysis of extensive phenomena.
In this study, we report the results of our attempt at a comprehensive reaction analysis of wet etching, combining PFP implemented in MatlantisTM<\/sup> and the SC-AFIR method implemented in GRRM20, which enable us to explore complex, multi-step reactions involving multiple molecules. We report the comparison results of reaction pathways considering the stabilization of intermediates by surrounding HF and H2O in the four-step fluorination reaction of Si.<\/p>\n\n\n\n

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\u677e\u672c \u7693\u592a<\/strong>
\u5317\u6d77\u9053\u5927\u5b66\u5927\u5b66\u9662\u7dcf\u5408\u5316\u5b66\u9662\u4fee\u4e86\u5f8c\u3001ENEOS\u682a\u5f0f\u4f1a\u793e\u306b\u5165\u793e\u3057\u8a08\u7b97\u5316\u5b66\u306b\u3088\u308b\u6750\u6599\u958b\u767a\u306e\u7814\u7a76\u304a\u3088\u3073Matlantis\u691c\u8a3c\u958b\u767a\u3001\u306a\u3089\u3073\u306bGRRM20 with Matlantis\u306e\u30a4\u30f3\u30bf\u30fc\u30d5\u30a7\u30fc\u30b9\u958b\u767a\u306b\u643a\u308f\u308b\u3002\u305d\u306e\u5f8cPFCC\u306b\u5165\u793e\u3002\u73fe\u5728\u306f\u30ab\u30b9\u30bf\u30de\u30fc\u30b5\u30af\u30bb\u30b9\u30a8\u30f3\u30b8\u30cb\u30a2\u3068\u3057\u3066Matlantis\u3092\u4f7f\u7528\u3057\u305f\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u958b\u767a\u306b\u5f93\u4e8b\u3002<\/p>\n<\/div>\n\n\n\n

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

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

2024\u5e7412\u67083\u65e5<\/h3>\n\n\n\n

\u30bb\u30c3\u30b7\u30e7\u30f3:<\/strong> MT04.05.19
\u65e5\u6642:<\/strong> December 3, 2024 from 8:00 PM to 10:00 PM EST
\u30eb\u30fc\u30e0<\/strong>: Hynes, Level 1, Hall A
\u30bb\u30c3\u30b7\u30e7\u30f3\u30bf\u30a4\u30d7:<\/strong> Poster Presentation
\u30bf\u30a4\u30c8\u30eb:<\/strong> High-Throughput Computational Search for Stable Compositions and Configurations in High-Entropy Perovskite SrTiO3
\u767a\u8868\u8005:<\/strong> Hiroki Kotaka, Yosuke Harashima, Hiroki Iriguchi, Tomoaki Takayama, Shogo Takasuka, Mikiya Fujii
\u7d44\u7e54:<\/strong> ENEOS Corporation & Nara Institute of Science and Technology<\/p>\n\n\n\n

2024\u5e7412\u67084\u65e5 <\/strong><\/h3>\n\n\n\n

\u30bb\u30c3\u30b7\u30e7\u30f3<\/strong> :<\/strong>\u00a0MT04.06.10
\u65e5\u6642:<\/strong>\u00a0December 4, 2024, 11:00 AM EST
\u30eb\u30fc\u30e0<\/strong>: Hynes, Level 2, Room 210
\u30bb\u30c3\u30b7\u30e7\u30f3\u30bf\u30a4\u30d7:<\/strong>\u00a0Oral Presentation
\u30bf\u30a4\u30c8\u30eb:<\/strong>\u00a0Multi-Objective Bayesian Optimization for Materials Discovery with Neural Network Potential\u2014An Application to Li-Ion Battery Cathode Material
\u767a\u8868\u8005:<\/strong>\u00a0Shuhei Watanabe, Hideaki Imamura, Chikashi Shinagawa, Kohei Shinohara, So Takamoto, Ju Li
\u7d44\u7e54:<\/strong>\u00a0Preferred Networks, inc. & Massachusetts Institute of Technology<\/p>\n\n\n\n

\u30bb\u30c3\u30b7\u30e7\u30f3<\/strong><\/strong> :<\/strong>\u00a0MT04.09.08
\u65e5\u6642:<\/strong>\u00a0December 4, 2024 from 8:00 PM to 10:00 PM EST
\u30eb\u30fc\u30e0:<\/strong> Hynes, Level 1, Hall A
\u30bb\u30c3\u30b7\u30e7\u30f3\u30bf\u30a4\u30d7:<\/strong>\u00a0Poster Presentation
\u30bf\u30a4\u30c8\u30eb:<\/strong>\u00a0Accelerating Advanced Material Design Through Versatile Atomistic Scale AI Simulator MATLANTIS
\u767a\u8868\u8005:<\/strong>\u00a0Yuji Hakozaki, Tasuku Onodera, Takashi Kojima
\u7d44\u7e54:<\/strong>\u00a0ENEOS Corporation<\/p>\n\n\n\n

\u30bb\u30c3\u30b7\u30e7\u30f3:<\/strong>\u00a0MT04.09.11
\u65e5\u6642:<\/strong>\u00a0December 4, 2024 from 8:00 PM to 10:00 PM EST
\u30eb\u30fc\u30e0:<\/strong> Hynes, Level 1, Hall A
\u30bb\u30c3\u30b7\u30e7\u30f3\u30bf\u30a4\u30d7:<\/strong>\u00a0Poster Presentation
\u30bf\u30a4\u30c8\u30eb:<\/strong>\u00a0Investigation of Bayesian Optimization and Generative Model for Crystal Structure Prediction of Molecular Crystals
\u767a\u8868\u8005:<\/strong>\u00a0Takuya Taniguchi
\u7d44\u7e54:<\/strong>\u00a0Waseda University<\/p>\n\n\n\n

2024\u5e7412\u67085\u65e5 <\/strong><\/h3>\n\n\n\n

\u30bb\u30c3\u30b7\u30e7\u30f3:<\/strong>\u00a0MT04.10.01
\u65e5\u6642:<\/strong>\u00a0December 5, 2024, 8:00 AM EST
\u30eb\u30fc\u30e0:<\/strong> Hynes, Level 2, Room 210
\u30bb\u30c3\u30b7\u30e7\u30f3\u30bf\u30a4\u30d7:<\/strong>\u00a0Oral Presentation
\u30bf\u30a4\u30c8\u30eb:<\/strong>\u00a0Flow for Generating Reaction Pathways and Validation of the Trained Neural Network
\u767a\u8868\u8005:<\/strong>\u00a0Akihide Hayashi, So Takamoto, Ju Li, Hirotaka Akita, Daisuka Okanohara
\u7d44\u7e54:<\/strong>\u00a0Preferred Networks, inc. & Massachusetts Institute of Technology<\/p>\n\n\n\n

\u30bb\u30c3\u30b7\u30e7\u30f3:<\/strong>\u00a0MT04.11.03
\u65e5\u6642:<\/strong>\u00a0December 5, 2024, 2:15 PM EST
\u30eb\u30fc\u30e0:<\/strong> Hynes, Level 2, Room 210
\u30bb\u30c3\u30b7\u30e7\u30f3\u30bf\u30a4\u30d7:<\/strong>\u00a0Oral Presentation
\u30bf\u30a4\u30c8\u30eb:<\/strong>\u00a0Towards r2SCAN-level Universal Neural Network Potential for Materials Discovery
\u767a\u8868\u8005:<\/strong>\u00a0Chikashi Shinagawa, So Takamoto, Daiki Shintani, Katsuhiko Nishimra, Ju Li
\u7d44\u7e54:<\/strong>\u00a0Preferred Networks, inc. & Massachusetts Institute of Technology<\/p>\n\n\n\n

\u30bb\u30c3\u30b7\u30e7\u30f3:<\/strong>\u00a0MT04.11.04
\u65e5\u6642:<\/strong>\u00a0December 5, 2024, 2:30 PM EST
\u30eb\u30fc\u30e0:<\/strong> Hynes, Level 2, Room 210
\u30bb\u30c3\u30b7\u30e7\u30f3\u30bf\u30a4\u30d7:<\/strong>\u00a0Oral Presentation
\u30bf\u30a4\u30c8\u30eb:<\/strong>\u00a0Support for 96 Elements and Improved Robustness of Universal Neural Network Potential PFP
\u767a\u8868\u8005:<\/strong>\u00a0So Takamoto, Chikashi Shinagawa, Daiki Shintani, Katsuhiko Nishimra, Ju Li
\u7d44\u7e54:<\/strong>\u00a0Preferred Networks, inc. & Massachusetts Institute of Technology <\/p>\n\n\n\n

\u30bb\u30c3\u30b7\u30e7\u30f3:<\/strong>\u00a0MT04.11.08
\u65e5\u6642:<\/strong>\u00a0December 5, 2024, 4:15 PM EST
\u30eb\u30fc\u30e0:<\/strong> Hynes, Level 2, Room 210
\u30bb\u30c3\u30b7\u30e7\u30f3\u30bf\u30a4\u30d7:<\/strong>\u00a0Oral Presentation
\u30bf\u30a4\u30c8\u30eb:<\/strong>\u00a0LightPFP\u2014Accelerating the Development of Task-Specific Machine Learning Potentials Using Universal Potential
\u767a\u8868\u8005:<\/strong>\u00a0Wenwen Li, Nontawat Charoenphakdee, Yuta Tsuboi, So Takamoto, Ju Li
\u7d44\u7e54:<\/strong>\u00a0Preferred Networks, inc. & Massachusetts Institute of Technology <\/p>\n\n\n\n

2024\u5e7412\u67086\u65e5 <\/h3>\n\n\n\n

\u30bb\u30c3\u30b7\u30e7\u30f3:<\/strong>\u00a0MT04.12.07
\u65e5\u6642:<\/strong>\u00a0December 6, 2024, 9:30 AM EST
\u30eb\u30fc\u30e0:<\/strong> Hynes, Level 2, Room 210
\u30bb\u30c3\u30b7\u30e7\u30f3\u30bf\u30a4\u30d7:<\/strong>\u00a0Oral Presentation
\u30bf\u30a4\u30c8\u30eb:<\/strong>\u00a0Accelerated Quantum Chemical Simulations for Oxygen Evolution Reaction Catalysts Using \u201cPreFerred Potential\u201d (PFP)\u2014A Pathway to Efficient Material Design
\u767a\u8868\u8005:<\/strong>\u00a0Hiroki Kotaka, Yuji Hakozaki, Terumasa Shimada, Yoichiro Kawami, Yoshitatsu Misu, Atsushi Fukazawa, Yusuke Hasegawa
\u7d44\u7e54:<\/strong>\u00a0ENEOS Corporation <\/p>\n\n\n\n

\u30bb\u30c3\u30b7\u30e7\u30f3:<\/strong>\u00a0MT04.12.11
\u65e5\u6642:<\/strong>\u00a0December 6, 2024, 11:00 AM EST
\u30eb\u30fc\u30e0:<\/strong> Hynes, Level 2, Room 210
\u30bb\u30c3\u30b7\u30e7\u30f3\u30bf\u30a4\u30d7:<\/strong>\u00a0Oral Presentation
\u30bf\u30a4\u30c8\u30eb:<\/strong>\u00a0Finite-Temperature Crystal Structure Prediction with Universal Neural Network Potential and Free Energy Calculation
\u767a\u8868\u8005:<\/strong>\u00a0Kohei Shinohara, Takuya Shibayama, Hideaki Imamura, Katsuhiko Nishimra, Chikashi Shinagawa, So Takamoto, Ju Li
\u7d44\u7e54:<\/strong>\u00a0Preferred 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) >> <\/a>2024 MRS Fall Meeting & Exhibit<\/a> <\/p>\n","protected":false},"featured_media":3140,"template":"","meta":{"_acf_changed":false},"event_seminar_article_category":[72,71],"event_seminar_type_category":[79],"class_list":["post-3066","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-exhibition"],"acf":[],"_links":{"self":[{"href":"https:\/\/matlantis.com\/ja\/wp-json\/wp\/v2\/event_seminar\/3066","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\/3140"}],"wp:attachment":[{"href":"https:\/\/matlantis.com\/ja\/wp-json\/wp\/v2\/media?parent=3066"}],"wp:term":[{"taxonomy":"event_seminar_article_category","embeddable":true,"href":"https:\/\/matlantis.com\/ja\/wp-json\/wp\/v2\/event_seminar_article_category?post=3066"},{"taxonomy":"event_seminar_type_category","embeddable":true,"href":"https:\/\/matlantis.com\/ja\/wp-json\/wp\/v2\/event_seminar_type_category?post=3066"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}