{"id":9297,"date":"2026-10-15T13:00:00","date_gmt":"2026-10-15T04:00:00","guid":{"rendered":"https:\/\/aizoth.com\/?p=9297"},"modified":"2026-10-14T13:38:28","modified_gmt":"2026-10-14T04:38:28","slug":"jimm2026fall_report","status":"publish","type":"post","link":"https:\/\/aizoth.com\/en\/2026\/10\/jimm2026fall_report\/","title":{"rendered":"AIZOTH Showcases AI-Driven Materials R&D at the JIMM 2026 Autumn Meeting"},"content":{"rendered":"\n

AIZOTH Inc. participated in the JIMM 2026 Autumn Meeting (179th Annual Meeting)<\/a>, organized by the Japan Institute of Metals and Materials (JIMM) and held at the Tegata Campus of Akita University from September 23 to 25, 2026. Through a booth exhibition and a research presentation, we shared how AI can support metals and materials research and discussed data-driven approaches with researchers and engineers from academia, industry, and public research institutions.<\/p>\n\n\n\n

Booth Exhibition: AI Tools for Materials Research and Simulation<\/h2>\n\n\n
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At our booth, we introduced our no-code AI analytics platform, Multi-Sigma\u00ae<\/a>, along with Multi-Sigma-Alchemy<\/a>, a dedicated tool for materials and drug discovery, and Multi-Sigma-Architect<\/a>, which builds surrogate models from CAE simulation results. We presented each tool through demonstrations and application examples.<\/p>\n\n\n\n

Visitors came from a wide range of backgrounds. They included researchers and students working on metallic materials at universities and research institutions, R&D professionals from steel, nonferrous metal, electronics, and analytical instrument manufacturers, and staff from public industrial research institutes.<\/p>\n\n\n\n

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Research Presentation: Materials Informatics with Limited Experimental Data<\/h2>\n\n\n\n

On September 25, we presented \u201cAdvancing Metal R&D with Limited Experimental Data through Materials Informatics.\u201d The presentation showed how AI can accelerate metals R&D even when only a small amount of experimental data is available, with examples including aluminum process optimization, thermal property prediction, surrogate modeling of thermomechanical simulations, and hardness prediction from metal microstructure images.<\/p>\n\n\n\n

Key Topics and Insights from Visitors<\/h2>\n\n\n\n

Many visitors wanted to know how machine learning could be applied to metals research and asked to see concrete application examples. Among those already using data analysis in their R&D, Multi-Sigma-Architect<\/a> attracted particular interest for its ability to build surrogate models from FEM, CFD, and other simulation results as well as from experimental data. The materials research functions of Multi-Sigma-Alchemy<\/a> also drew particular interest.<\/p>\n\n\n\n

Using posters and leaflets, we walked visitors through how surrogate models are built and put to use. Several visitors asked practical questions with day-to-day operation in mind, such as how geometry and physical fields like stress and temperature are handled, and in what formats analysis results can be exchanged. We also discussed the thinking behind the methods themselves, including how physics-based modeling differs from learning input\u2013output relationships directly from data, and why models can be built from relatively small datasets.<\/p>\n\n\n\n

One distinctive feature of this meeting was the variety of data that visitors brought to the conversation. Beyond the relationship between composition and properties, they asked how to apply AI to analytical and measurement data, images, structural information on materials, and manufacturing process data. We explored approaches suited to each research theme, and for manufacturing process data, we also discussed applications in process informatics as well as in materials development.<\/p>\n\n\n\n

We also spoke with visitors who oversee a broad range of technical themes within their organizations and with those who provide technical support to companies, who were looking for a wider view of trends in simulation and data utilization. We were also grateful to welcome back visitors who knew of our work from previous talks and conferences.<\/p>\n\n\n\n

Thank You for Visiting<\/h2>\n\n\n\n

The JIMM 2026 Autumn Meeting gave us the opportunity to speak directly with a wide range of people, from researchers and students engaged in fundamental research at universities and research institutions to engineers developing materials at companies. We would like to thank everyone who visited our booth and attended our research presentation.<\/p>\n\n\n\n

We have prepared various leaflets introducing analysis examples using Multi-Sigma\u00ae<\/a>, the AI analytics platform developed by AIZOTH. These leaflets can be downloaded from the page below.<\/p>\n\n\n\n

https:\/\/aizoth.com\/en\/service\/multi-sigma\/download\/<\/a><\/p>\n\n\n\n

For any questions or consultations regarding not only our products but also joint research, consulting services, or materials R&D using AI and machine learning, please contact us using the inquiry form below.<\/p>\n\n\n\n

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Contact Us<\/strong><\/a><\/div>\n<\/div>\n\n\n\n

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