5 citations · 8 across the 8 of their papers we have counts for
10 papers
AutoSchema: Live Schema Grounding for Agentic Text-to-Sparql over Heterogeneous Knowledge Graphs
Yiming Zhang, Koji Tsuda
Life science knowledge graphs make large collections of structured data available through SPARQL, but each resource uses its own schema, identifiers, and links. TogoMCP helps langu…
SAGA: Schema-Aware Grounding for Agentic Text-to-SPARQL Generation
Yiming Zhang, Koji Tsuda
Complex knowledge base question answering (KBQA) is commonly approached through either information retrieval over a question-specific subgraph or semantic parsing into an executabl…
NIMO: A Software Platform for Closed-Loop Materials Exploration with Diverse AI Algorithms
Ryo Tamura, Naruki Yoshikawa, Koji Tsuda +1
Self-driving laboratories (SDLs), where artificial intelligence proposes subsequent experiments and robotic systems execute them, are rapidly becoming the vanguard of materials dis…
ProvMind: Provenance-grounded reasoning for materials synthesis
Yiming Zhang, Ryo Tamura, Koji Tsuda
Materials process optimization requires reasoning over routes, conditions, tools and causal dependencies, yet most computational formulations flatten synthesis procedures into text…
LLM-guided phase diagram construction through high-throughput experimentation
Ryo Tamura, Haruhiko Morito, Yuna Oikawa +7
Constructing phase diagrams for multicomponent alloys requires extensive experimental measurements and is a time-consuming task. Here we investigate whether large language models (…
Update of PHYSBO: Improving Usability and Portability of Bayesian Optimization for Physics and Materials Research
Yuichi Motoyama, Kazuyoshi Yoshimi, Tatsumi Aoyama +3
Bayesian optimization (BO) is widely used to accelerate physics and materials research, where objective function evaluations are computationally or experimentally expensive. While…
aLLoyM: A large language model for alloy phase diagram prediction
Yuna Oikawa, Guillaume Deffrennes, Taichi Abe +2
Large Language Models (LLMs) are general-purpose tools with wide-ranging applications, including in materials science. In this work, we introduce aLLoyM, a fine-tuned LLM specifica…
Black-box optimization using factorization and Ising machines
Ryo Tamura, Yuya Seki, Yuki Minamoto +4
Black-box optimization (BBO) is used in materials design, drug discovery, and hyperparameter tuning in machine learning. The world is experiencing several of these problems. In thi…
CRYSIM: Prediction of Symmetric Structures of Large Crystals with GPU-based Ising Machines
Chen Liang, Diptesh Das, Jiang Guo +3
Solving black-box optimization problems with Ising machines is increasingly common in materials science. However, their application to crystal structure prediction (CSP) is still i…
Preference-Optimized Pareto Set Learning for Blackbox Optimization
Zhang Haishan, Diptesh Das, Koji Tsuda
Multi-Objective Optimization (MOO) is an important problem in real-world applications. However, for a non-trivial problem, no single solution exists that can optimize all the objec…