Publications

(2019). Monte Carlo tree search for materials design and discovery. MRS Communications, 9, 532-536.

DOI

(2019). Machine-Learning-Assisted Development and Theoretical Consideration for the Al2Fe3Si3 Thermoelectric Material. ACS Applied Materials and Interfaces, 11, 11545-11554.

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(2019). Application of Bayesian Optimization for Pharmaceutical Product Development. Journal of Pharmaceutical Innovation, 1-11.

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(2019). Efficient construction method for phase diagrams using uncertainty sampling. Physical Review Materials, i3, 3, 033802.

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(2019). An interpretable machine learning model for diagnosis of Alzheimer's disease. PeerJ Life & Environment, 7, e6543.

DOI

(2019). Expanding the horizon of automated metamaterials discovery via quantum annealing. arXiv:1902.06573.

arXiv

(2019). Ultranarrow-Band Wavelength-Selective Thermal Emission with Aperiodic Multilayered Metamaterials Designed by Bayesian Optimization. ACS Central Science, 5, 2, 319-326.

DOI

(2019). Data-driven analysis of electron relaxation times in PbTe-type thermoelectric materials. Science and Technology of Advanced Materials, 20, 511-520.

DOI

(2018). Legendre Decomposition for Tensors. Advances in Neural Information Processing Systems, 31, 8811-8821.

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(2018). Improving the Accuracy of Protein‐Ligand Binding Mode Prediction Using a Molecular Dynamics‐Based Pocket Generation Approach. Journal of computational chemistry, 39, 32, 2679-2689.

DOI

(2018). Data-driven approach for the prediction and interpretation of core-electron loss spectroscopy. Scientific Reports, 8, 13548.

DOI

(2018). DenseZDD: A Compact and Fast Index for Families of Sets. Algorithms, 11, 128.

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(2018). Functional Nanoparticles-Coated Nanomechanical Sensor Arrays for Machine Learning-Based Quantitative Odor Analysis. ACS Sensors, 3, 8, 1592-1600.

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(2018). Machine-Learning-Guided Mutagenesis for Directed Evolution of Fluorescent Proteins. ACS Synthetic Biology, 7, 2014-2022.

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(2018). Fine-grained optimization method for crystal structure prediction. npj Computational Materials, 4, 32.

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(2018). Role of linkage structures in supply chain for managing greenhouse gas emissions. Journal of Economic Structures, 7, 1, 7.

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(2018). Structure prediction of boron-doped graphene by machine learning. The Journal of Chemical Physics, 148, 241716.

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(2018). Machine Learning Accelerates MD-based Binding-Pose Prediction between Ligands and Proteins. Bioinformatics, 34, 770-778.

DOI

(2018). Machine Learning-Based Experimental Design in Materials Science. In: Tanaka I. (eds) Nanoinformatics.Springer, Singapore, pp. 65-74.

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(2018). Crystal structure prediction accelerated by Bayesian optimization. Physical Review Materials, 2, 013803.

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