98 citations · 276 across the 11 of their papers we have counts for
12 papers
Kinetic Monte Carlo-Ising Machine Optimization for Atomistic Inverse Design of Solid Electrolytes
Ai Koizumi, Tomofumi Tada, Ryo Tamura
Maximizing ionic conductivity remains a fundamental challenge in the atomistic design of solid electrolytes. To this end, we present a framework that combines kinetic Monte Carlo (…
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…
NIMO Controller: a self-driving laboratory orchestrator based on the Model Context Protocol
Naruki Yoshikawa, Ryo Tamura
Self-driving laboratories (SDLs) are attracting increasing attention as a means of accelerating scientific discovery; however, developing SDL software remains technically demanding…
Data-driven study of the enthalpy of mixing in the liquid phase
Guillaume Deffrennes, Bengt Hallstedt, Taichi Abe +5
The enthalpy of mixing in the liquid phase is a thermodynamic property reflecting interactions between elements that is key to predict phase transformations. Widely used models exi…
Boltzmann sampling with quantum annealers via fast Stein correction
Ryosuke Shibukawa, Ryo Tamura, Koji Tsuda
Despite the attempts to apply a quantum annealer to Boltzmann sampling, it is still impossible to perform accurate sampling at arbitrary temperatures. Conventional distribution cor…
NIMS-OS: An automation software to implement a closed loop between artificial intelligence and robotic experiments in materials science
Ryo Tamura, Koji Tsuda, Shoichi Matsuda
NIMS-OS (NIMS Orchestration System) is a Python library created to realize a closed loop of robotic experiments and artificial intelligence (AI) without human intervention for auto…
A machine learning-based classification approach for phase diagram prediction
Guillaume Deffrennes, Kei Terayama, Taichi Abe +1
Knowledge of phase diagrams is essential for material design as it helps in understanding microstructure evolution during processing. The determination of phase diagrams is thus on…
Bayesian optimization package: PHYSBO
Yuichi Motoyama, Ryo Tamura, Kazuyoshi Yoshimi +3
PHYSBO (optimization tools for PHYSics based on Bayesian Optimization) is a Python library for fast and scalable Bayesian optimization. It has been developed mainly for application…
Unsupervised learning-based structural analysis: Search for a characteristic low-dimensional space by local structures in atomistic simulations
Ryo Tamura, Momo Matsuda, Jianbo Lin +3
Owing to the advances in computational techniques and the increase in computational power, atomistic simulations of materials can simulate large systems with higher accuracy. Compl…
Optimization of heterogeneous ternary Li3PO4-Li3BO3-Li2SO4 mixture for Li-ion conductivity by machine learning
Kenji Homma, Yu Liu, Masato Sumita +5
Mixing heterogeneous Li-ion conductive materials is one of potential ways to enhance the Li-ion conductivity more than that of the parent materials. However, the development of the…
Leveraging Legacy Data to Accelerate Materials Design via Preference Learning
Xiaolin Sun, Zhufeng Hou, Masato Sumita +3
Machine learning applications in materials science are often hampered by shortage of experimental data. Integration with legacy data from past experiments is a viable way to solve…
Efficient Construction Method for Phase Diagrams Using Uncertainty Sampling
Kei Terayama, Ryo Tamura, Yoshitaro Nose +4
We develop a method to efficiently construct phase diagrams using machine learning. Uncertainty sampling (US) in active learning is utilized to intensively sample around phase boun…