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  6. AI Material Exploration Video | Property Prediction and Experimental Planning

AI Material Exploration Video | Property Prediction and Experimental Planning

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last updated:Mar 17, 2026

シュレーディンガー
シュレーディンガー

Free viewing now available | Practical demo video on streamlining property prediction and experimental planning with AI

The use of AI in materials exploration is becoming a global standard; however, there are still many cases where implementation is not progressing due to reasons such as "difficult to operate" and "lack of data science personnel." Schrödinger has made a webinar video (with Japanese subtitles) available for free to address these challenges. You can watch it immediately after registering through the form. ▼ You can watch it right away by registering through the form below ▼ https://info.schrodinger.com/ms-psb-01-submit-form In the video, we demonstrate how to perform advanced property predictions and machine learning-based experimental design (ML-based DoE) using Schrödinger's materials development simulation platform, 'Materials Science Suite,' even without specialized knowledge. We will introduce practical examples, such as approaches to derive "the next experiment to conduct" from small data sets, viscosity predictions of binary mixtures, and optimization of complex multi-component formulations. First, experience the acceleration of research and development through the use of AI in the video.

    simulatorStructural AnalysisStress Analysis
2校_0725_schrodinger_550_550_2091471.jpg

AI Material Exploration Video | Property Prediction and Experimental Planning

2校_0725_schrodinger_550_550_2091471.jpg
2校_0725_schrodinger_550_550_2091471.jpg
  • Related Link - https://info.schrodinger.com/ms-psb-01-submit-form

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【Main Demonstration Content】 - Viscosity Prediction of Binary Liquids: A workflow for constructing a high-precision viscosity prediction ML model and applying it to various material applications. - Optimization and Scale-Up of Complex Compositions: A process that predicts the behavior of complex multicomponent systems, such as shampoos, using an ML model and proposes the optimal experimental conditions (Next Best Experiment) to try next. 【Notes for Viewing】 Regarding Automatic Translation Subtitles: The subtitles for this video are generated using automatic translation, so there may be some unnatural expressions in certain technical terms. For Detailed Information Inquiries: Please feel free to contact us by replying to this email regarding the technical background of the video content or specific applications to your company's challenges. Viewing Period: The publication of this video may end without prior notice. Thank you for your understanding.

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This is a Japanese brochure that clearly introduces Schrödinger's materials development support products.

This is a Japanese brochure that clearly introduces Schrödinger's materials development support products.

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Japanese Brochure: Polymer and Resin Property Value Prediction Support Tool

Japanese Brochure: Polymer and Resin Property Value Prediction Support Tool

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[Data] Advanced physics-based modeling and AI/machine learning software for materials science.

[Data] Advanced physics-based modeling and AI/machine learning software for materials science.

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Schrödinger's platform for materials science

Schrödinger's platform for materials science

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Schrödinger Co., Ltd. is the Japanese subsidiary of Schrödinger Inc., headquartered in New York, USA. Schrödinger has a history of about 30 years in developing software that integrates advanced technologies in chemistry and computer science, primarily in the fields of materials science and life sciences, providing advanced solutions for drug discovery, biologics, and materials research and development.

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