Publications (7)
Cost-effective search for lower-error region in material parameter space using multifidelity Gaussian process modeling
Shion Takeno, Yuhki Tsukada, Hitoshi Fukuoka +3
Information regarding precipitate shapes is critical for estimating material parameters. Hence, we considered estimating a region of material parameter space in which a computation…
Ring-originated anisotropy of local structural ordering in amorphous and crystalline silicon dioxide
Motoki Shiga, Akihiko Hirata, Yohei Onodera +1
Rings comprising chemically bonded atoms are essential topological motifs for the structural ordering of network-forming materials. Quantification of such larger motifs beyond shor…
Coordinate Encoding on Linear Grids for Physics-Informed Neural Networks
Tetsuro Tsuchino, Motoki Shiga
In solving partial differential equations (PDEs), machine learning utilizing physical laws has received considerable attention owing to advantages such as mesh-free solutions, unsu…
Visual explanations of machine learning model estimating charge states in quantum dots
Yui Muto, Takumi Nakaso, Motoya Shinozaki +13
Charge state recognition in quantum dot devices is important in the preparation of quantum bits for quantum information processing. Toward auto-tuning of larger-scale quantum devic…
A machine learning-based selective sampling procedure for identifying the low energy region in a potential energy surface: a case study on proton conduction in oxides
Kazuaki Toyoura, Daisuke Hirano, Atsuto Seko +5
In this paper, we propose a selective sampling procedure to preferentially evaluate a potential energy surface (PES) in a part of the configuration space governing a physical prope…
Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its parallelization
Shion Takeno, Hitoshi Fukuoka, Yuhki Tsukada +4
In a standard setting of Bayesian optimization (BO), the objective function evaluation is assumed to be highly expensive. Multi-fidelity Bayesian optimization (MFBO) accelerates BO…
Exploring a potential energy surface by machine learning for characterizing atomic transport
Kenta Kanamori, Kazuaki Toyoura, Junya Honda +7
We propose a machine-learning method for evaluating the potential barrier governing atomic transport based on the preferential selection of dominant points for the atomic transport…