474 citations · 1.7k across the 15 of their papers we have counts for
15 papers
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…
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…
Probing conformational dynamics of antibodies with geometric simulations
Andrejs Tucs, Koji Tsuda, Adnan Sljoka
This chapter describes the application of constrained geometric simulations for prediction of antibody structural dynamics. We utilize constrained geometric simulations method FROD…
A generative model for molecule generation based on chemical reaction trees
Dai Hai Nguyen, Koji Tsuda
Deep generative models have been shown powerful in generating novel molecules with desired chemical properties via their representations such as strings, trees or graphs. However,…
Continuous black-box optimization with quantum annealing and random subspace coding
Syun Izawa, Koki Kitai, Shu Tanaka +2
A black-box optimization algorithm such as Bayesian optimization finds extremum of an unknown function by alternating inference of the underlying function and optimization of an ac…
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…
Deep learning-based quality filtering of mechanically exfoliated 2D crystals
Yu Saito, Kento Shin, Kei Terayama1 +7
Two-dimensional (2D) crystals are attracting growing interest in various research fields such as engineering, physics, chemistry, pharmacy and biology owing to their low dimensiona…
Expanding the horizon of automated metamaterials discovery via quantum annealing
Koki Kitai, Jiang Guo, Shenghong Ju +4
Complexity of materials designed by machine learning is currently limited by the inefficiency of classical computers. We show how quantum annealing can be incorporated into automat…
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…
Population-based de novo molecule generation, using grammatical evolution
Naruki Yoshikawa, Kei Terayama, Teruki Honma +2
Automatic design with machine learning and molecular simulations has shown a remarkable ability to generate new and promising drug candidates. Current models, however, still have p…
ChemTS: An Efficient Python Library for de novo Molecular Generation
Xiufeng Yang, Jinzhe Zhang, Kazuki Yoshizoe +2
Automatic design of organic materials requires black-box optimization in a vast chemical space. In conventional molecular design algorithms, a molecule is built as a combination of…
Designing nanostructures for interfacial phonon transport via Bayesian optimization
Shenghong Ju, Takuma Shiga, Lei Feng +3
We demonstrate optimization of thermal conductance across nanostructures by developing a method combining atomistic Green's function and Bayesian optimization. With an aim to minim…
Discovery of low thermal conductivity compounds with first-principles anharmonic lattice dynamics calculations and Bayesian optimization
Atsuto Seko, Atsushi Togo, Hiroyuki Hayashi +3
Compounds of low lattice thermal conductivity (LTC) are essential for seeking thermoelectric materials with high conversion efficiency. Some strategies have been used to decrease L…
Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single and binary component solids
Atsuto Seko, Tomoya Maekawa, Koji Tsuda +1
A combination of systematic density functional theory (DFT) calculations and machine learning techniques has a wide range of potential applications. This study presents an applicat…