4 papers
MaterialFigBENCH: benchmark dataset with figures for evaluating college-level materials science problem-solving abilities of multimodal large language models
Michiko Yoshitake, Yuta Suzuki, Ryo Igarashi +2
We present MaterialFigBench, a benchmark dataset designed to evaluate the ability of multimodal large language models (LLMs) to solve university-level materials science problems th…
Bridging Text and Crystal Structures: Literature-driven Contrastive Learning for Materials Science
Yuta Suzuki, Tatsunori Taniai, Ryo Igarashi +4
Understanding structure-property relationships is an essential yet challenging aspect of materials discovery and development. To facilitate this process, recent studies in material…
CrystalFramer: Rethinking the Role of Frames for SE(3)-Invariant Crystal Structure Modeling
Yusei Ito, Tatsunori Taniai, Ryo Igarashi +2
Crystal structure modeling with graph neural networks is essential for various applications in materials informatics, and capturing SE(3)-invariant geometric features is a fundamen…
MaterialBENCH: Evaluating College-Level Materials Science Problem-Solving Abilities of Large Language Models
Michiko Yoshitake, Yuta Suzuki, Ryo Igarashi +2
A college-level benchmark dataset for large language models (LLMs) in the materials science field, MaterialBENCH, is constructed. This dataset consists of problem-answer pairs, bas…