5 citations · 10 across the 7 of their papers we have counts for
7 papers
LLM-Guided Test-Time Discovery of Quantum-Chemical Approximation Algorithms
Masaya Hagai, Yuta Suzuki, Tomoya Murata +2
Quantum chemistry simulations underpin modern materials discovery, yet their impact is limited by steep computational cost and dependence on fixed approximation schemes. Foundation…
LapidaryEngine: Fully Conversational Crystal Generation
Yusei Ito, Yuta Suzuki, Tomoya Murata +1
The emergence of Large Language Models (LLMs) has inspired the vision of generating bespoke crystal materials directly from natural-language instructions, enabling users to design…
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…
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…
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…
Neural Structure Fields with Application to Crystal Structure Autoencoders
Naoya Chiba, Yuta Suzuki, Tatsunori Taniai +4
Representing crystal structures of materials to facilitate determining them via neural networks is crucial for enabling machine-learning applications involving crystal structure es…