Yusuke Hashimoto

日本語

Talks & Presentations

Invited talks, seminars, and conference presentations

November 10, 2025

AI and Robotics: Opening New Frontiers in Materials Science

Institute of Science Tokyo - Quantum Physics and Nanoscience Seminar
Invited Seminar
Institute of Science Tokyo seminar

Abstract

The rapid development of advanced technologies such as AI and robotic arms is spreading to many fields, bringing new perspectives and R&D methods to materials science. I will introduce the development of materials maps that integrate experimental and computational data through machine learning, automated synthesis of nanomaterials such as metal-organic frameworks (MOFs) using robotic arms, electric pipettes, and 3D printers, and autonomous data analysis methods using multi-agent systems with generative AI.

📄 Abstract (PDF)
October 30, 2025

Mapping Thermoelectric Materials Using Machine Learning on Integrated Computational and Experimental Datasets

IMPRES2025 (The 7th International Symposium on Innovative Materials and Processes in Energy Systems)
Keynote Lecture

Abstract

Presented a new mapping method for thermoelectric materials that integrates computational and experimental data using machine learning. This research enables visual representation of structural similarity in materials and rapid extraction of unknown high-performance materials.

🔗 IMPRES2025 Official Site
September 2025

Application of a Material Structural Similarity Map to Materials Process Exploration

Japan Society of Applied Physics (JSAP)
Research Presentation

Abstract

Presented a new approach to materials process exploration using material structural similarity maps. This research develops a method to visualize material structural similarity and enable efficient exploration of materials processes.

March 24, 2025

Local and Global Mapping of Thermoelectric Materials Based on Computational and Experimental Datasets

Stanford University - GLAM Special Seminar
Invited Seminar
Stanford University seminar

Abstract

Presented local and global mapping methods for thermoelectric materials based on computational and experimental data. Using machine learning-based materials mapping technology, we introduced a new approach to visualize structure-property correlations of thermoelectric materials and accelerate the search for high-performance materials.