1 citations · 1 across the 3 of their papers we have counts for
3 papers
Omics-scale polymer computational database transferable to real-world artificial intelligence applications
Ryo Yoshida, Yoshihiro Hayashi, Hidemine Furuya +103
Developing large-scale foundational datasets is a critical milestone in advancing artificial intelligence (AI)-driven scientific innovation. However, unlike AI-mature fields such a…
Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research
Kan Hatakeyama-Sato, Toshihiko Nishida, Kenta Kitamura +4
This review explores the potential of foundation models to advance laboratory automation in the materials and chemical sciences. It emphasizes the dual roles of these models: cogni…
SPACIER: On-Demand Polymer Design with Fully Automated All-Atom Classical Molecular Dynamics Integrated into Machine Learning Pipelines
Shun Nanjo, Arifin, Hayato Maeda +5
Machine learning has rapidly advanced the design and discovery of new materials with targeted applications in various systems. First-principles calculations and other computer expe…