98 citations · 276 across the 11 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…
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…