自著論文
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Data-driven analysis of electron relaxation times in PbTe-type thermoelectric materials
Science and Technology of Advanced Materials, 20, 1, 2019, pp. 511-520, 10.1080/14686996.2019.1603885
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Effects of data bias on machine-learning–based material discovery using experimental property data
Science and Technology of Advanced Materials: Methods, 2, 1, 2022, pp. 302-309, 10.1080/27660400.2022.2109447
Starrydataを引用している論文
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Thermophysical characterization of UFe3B2 and USiNi: An experimental study
Journal of Nuclear Materials, 595, 2024, 10.1016/j.jnucmat.2024.155048
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Dealing with the big data challenges in AI for thermoelectric materials [应对热电材料人工智能领域的大数据挑战]
Science China Materials, 67, 4, pp. 1173-1182, 2024, 10.1007/s40843-023-2777-2
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Structural, magnetic, electronic, optical and thermoelectric properties of the new CrEuAu2 compound
Chinese Journal of Physics, 88, pp. 913-921, 2024, 10.1016/j.cjph.2024.01.008
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Clustering method for the construction of machine learning model with high predictive ability
Chemometrics and Intelligent Laboratory Systems, 246, 2024, 10.1016/j.chemolab.2024.105084
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Evaluation and Optimization Methods for Applicability Domain Methods and Their Hyperparameters, Considering the Prediction Performance of Machine Learning Models
ACS Omega, 9, 10, pp. 11453-11458, 2024, 10.1021/acsomega.3c08036
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https://2DMat.ChemDX.org: Experimental data platform for 2D materials from synthesis to physical properties
Digital Discovery, 3, 3, pp. 573-585, 2024, 10.1039/d3dd00243h
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Are topological insulators promising thermoelectrics?
Materials Horizons, 11, 5, pp. 1188-1198, 2024, 10.1039/d3mh01930f
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Machine learning based feature engineering for thermoelectric materials by design
Digital Discovery, 3, 1, pp. 210-220, 2024, 10.1039/d3dd00131h
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Computational and Data-Driven Development of Thermoelectric Materials
Thermoelectric Micro/Nano Generators: Fundamental Physics, Materials and Measurements, pp. 17-70, 2024
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Not as simple as we thought: a rigorous examination of data aggregation in materials informatics
Digital Discovery, 3, 2, pp. 337-346, 2023, 10.1039/d3dd00207a
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Methods and applications of autonomous experimentation
Methods and Applications of Autonomous Experimentation, pp. 1-444, 2023, 10.1201/9781003359593
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Machine-learning-assisted discovery of 212-Zintl-phase compounds with ultra-low lattice thermal conductivity
Journal of Materials Chemistry A, 12, 2, pp. 1157-1165, 2023, 10.1039/d3ta05690b
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Small data machine learning in materials science
npj Computational Materials, 9, 1, 2023, 10.1038/s41524-023-01000-z
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Experiment and Theory in Concert To Unravel the Remarkable Electronic Properties of Na-Doped Eu11Zn4Sn2As12: A Layered Zintl Phase
Chemistry of Materials, 35, 18, pp. 7719-7729, 2023, 10.1021/acs.chemmater.3c01509
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A simple Pb-doping to achieve bonding evolution, VSn and resonant level shifting for regulating thermoelectric transport behavior of SnTe
Journal of Materials Science and Technology, 151, pp. 66-72, 2023, 10.1016/j.jmst.2022.12.021
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Interpretation of Machine Learning Models for Data Sets with Many Features Using Feature Importance
ACS Omega, 8, 25, pp. 23218-23225, 2023, 10.1021/acsomega.3c03722
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Best thermoelectric efficiency of ever-explored materials
iScience, 26, 4, 2023, 10.1016/j.isci.2023.106494
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TEXplorer.org: Thermoelectric material properties data platform for experimental and first-principles calculation results
APL Materials, 11, 4, 2023, 10.1063/5.0137642
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Quantifying the performance of machine learning models in materials discovery
Digital Discovery, 2, 2, pp. 327-338, 2023, 10.1039/d2dd00113f
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Recent advances and challenges in experiment-oriented polymer informatics
Polymer Journal, 55, 2, pp. 117-131, 2023, 10.1038/s41428-022-00734-9
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Thermodynamic and electron transport properties of Ca3Ru2 O7 from first-principles phonon calculations and Boltzmann transport theory
Physical Review B, 107, 3, 2023, 10.1103/PhysRevB.107.035118
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Developing a New Web Service for Experimental Nuclear Reaction Database (EXFOR) Using RESTful API and JSON
16th Varenna Conference on Nuclear Reaction Mechanisms, NRM2023, 2023, 10.1051/epjconf/202429212003
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A multiclass classification model for predicting the thermal conductivity of uranium compounds
Journal of Nuclear Science and Technology, 61, 6, pp. 778-788, 2024, 10.1080/00223131.2023.2269974
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A public database of thermoelectric materials and system-identified material representation for data-driven discovery
npj Computational Materials, 8, 1, 2022, 10.1038/s41524-022-00897-2
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Optical emissivity dataset of multi-material heterogeneous designs generated with automated figure extraction
Scientific Data, 9, 1, 2022, 10.1038/s41597-022-01699-3
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Y2Te3: A New n-Type Thermoelectric Material
ACS Applied Materials and Interfaces, 14, 38, pp. 43517-43526, 2022, 10.1021/acsami.2c12112
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Active learning for noisy physical experiments with more than two responses
Chemometrics and Intelligent Laboratory Systems, 226, 2022, 10.1016/j.chemolab.2022.104595
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A deep learning perspective into the figure-of-merit of thermoelectric materials
Materials Letters, 319, 2022, 10.1016/j.matlet.2022.132299
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Machine Learning Approaches for Accelerating the Discovery of Thermoelectric Materials
ACS Symposium Series, 1416, pp. 1-32, 2022, 10.1021/bk-2022-1416.ch001
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Effective Mass from Seebeck Coefficient
Advanced Functional Materials, 32, 20, 2022, 10.1002/adfm.202112772
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Artificial intelligence for search and discovery of quantum materials
Communications Materials, 2, 1, 2021, 10.1038/s43246-021-00209-z
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Data-driven thermoelectric modeling: Current challenges and prospects
Journal of Applied Physics, 130, 19, 2021, 10.1063/5.0054532
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Lifting the limitations of Gaussian mixture regression through coupling with principal component analysis and deep autoencoding
Chemometrics and Intelligent Laboratory Systems, 218, 2021, 10.1016/j.chemolab.2021.104437
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Physical Insights on the Lattice Softening Driven Mid-Temperature Range Thermoelectrics of Ti/Zr-Inserted SnTe—An Outlook Beyond the Horizons of Conventional Phonon Scattering and Excavation of Heikes’ Equation for Estimating Carrier Properties
Advanced Energy Materials, 11, 28, 2021, 10.1002/aenm.202101122
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Design of thermoelectric materials with high electrical conductivity, high Seebeck coefficient, and low thermal conductivity
Analytical Science Advances, 2, 5-6, pp. 289-294, 2021, 10.1002/ansa.202000114
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Recent advances and future prospects in energy harvesting technologies
Japanese Journal of Applied Physics, 59, 11, 2020, 10.35848/1347-4065/abbfa0
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Direct inverse analysis based on Gaussian mixture regression for multiple objective variables in material design
Materials and Design, 196, 2020, 10.1016/j.matdes.2020.109168
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Monolayer Ag2S: Ultralow lattice thermal conductivity and excellent thermoelectric performance
ACS Applied Energy Materials, 3, 10, pp. 10147-10153, 2020, 10.1021/acsaem.0c01844
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Weighted Mobility
Advanced Materials, 32, 25, 2020, 10.1002/adma.202001537
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Machine Learning Approaches for Thermoelectric Materials Research
Advanced Functional Materials, 30, 5, 2020, 10.1002/adfm.201906041