activity
20232026
most citedCrystalformer: Infinitely Connected Attention for Periodic Structure Encoding

5 citations · 7 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CL2026

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…

cs.LG2025

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…

cs.LG2025★ 2 cited

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…

cs.CL2024

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…

cs.LG2024★ 5 cited

Crystalformer: Infinitely Connected Attention for Periodic Structure Encoding

Tatsunori Taniai, Ryo Igarashi, Yuta Suzuki +4

Predicting physical properties of materials from their crystal structures is a fundamental problem in materials science. In peripheral areas such as the prediction of molecular pro…

cs.LG2023

A Transformer Model for Symbolic Regression towards Scientific Discovery

Florian Lalande, Yoshitomo Matsubara, Naoya Chiba +3

Symbolic Regression (SR) searches for mathematical expressions which best describe numerical datasets. This allows to circumvent interpretation issues inherent to artificial neural…