業績

(2020). Designing metamaterials with quantum annealing and factorization machines. Phys. Rev. Research 2, 013319.

DOI arXiv

(2019). Deep-learning-based quality filtering of mechanically exfoliated 2D crystals. npj Computational Materials, 5, 124.

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(2019). Efficient query autocompletion with edit distance-based error tolerance. The VLDB Journal.

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(2019). Legendre decomposition for tensors. Journal of Statistical Mechanics, 124017.

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(2019). evERdock BAI: Machine-learning-guided selection of protein-protein complex structure. Journal of Chemical Physics, 151, 215104.

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

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(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.

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

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(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.

DOI

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

DOI

(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.

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(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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(2017). ChemTS: an efficient python library for de novo molecular generation. Science, Technology of Advanced Materials, 18, 972-976.

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(2017). Transfer Learning to Accelerate Interface Structure Searches. Journal of the Physical Society of Japan, 86, 123601.

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(2017). Machine learning reveals orbital interaction in materials. Science, Technology of Advanced Materials, 18, 756-765.

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(2017). Tensor Balancing on Statistical Manifold. Proceedings of the 34th International Conference on Machine Learning (ICML 2017), pp. 3270-3279.

(2017). Selective Inference for Sparse High-Order Interaction Models. Proceedings of the 34th International Conference on Machine Learning (ICML 2017), pp. 3338-3347.

(2017). MDTS: automatic complex materials design using Monte Carlo tree search. Science, Technology of Advanced Materials, 18, 498-503.

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(2017). Designing Nanostructures for Phonon Transport via Bayesian Optimization. Physical Review X, 7, 021024.

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(2017). 高次交互作用モデリングのための機械学習アルゴリズム. 日本ロボット学会誌, 3, 215-220.

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(2016). Matrix Factorization for Automatic Chemical Mapping from Electron Microscopic Spectral Imaging Datasets. Transactions of the Materials Research Society of Japan, 41, 333-336.

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(2016). Quantifying Genomic Privacy in Genetic Test Results. 3rd International Workshop on Genome Privacy Security (Genopri 2016).

(2016). Sparse Modeling of EELS and EDX Spectral Imaging Data by Nonnegative Matrix Factorization. Ultramicroscopy, 170, 43-59.

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(2016). DAMAS: An Annotation Support Tool for Materials Information. In Proceeding of the 5th Asian Materials Data Symposium (AMDS2016), pp. 242-245, Hanoi, Vietnam. Oct.30 – Nov. 2.

(2016). Information Decomposition on Structured Space. 2016 IEEE International Symposium on Information Theory, pages 575-579.

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(2016). Safe Pattern Pruning: An Efficient Approach for Predictive Pattern Mining. 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pages 1785-1794.

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(2016). Machine-learning prediction of d-band center for metals and bimetals. RSC Advances, 6, 52587-52595.

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(2016). Significant Pattern Mining with Confounding Variables. 20th Pacific Asia Conference on Knowledge Discovery, Data Mining (PAKDD), pp. 277-289.

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(2016). Acceleration of stable interface structure searching using a kriging approach . Japanese Journal of Applied Physics, 55, 045502.

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