自著論文
Starrydataを引用している論文
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Small data machine learning in materials science
npj Computational Materials, 9(1), 42, 2023, 10.1038/s41524-023-01000-z
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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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Best thermoelectric efficiency of ever-explored materials
iScience, 26(4), 106494, 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), 041111, 2023, 10.1063/5.0137642
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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), 035118, 2023, 10.1103/PhysRevB.107.035118
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A public database of thermoelectric materials and system-identified material representation for data-driven discovery
npj Computational Materials, 8(1), 214, 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), 589, 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, 104595, 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, 132299, 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), 2112772, 2022, 10.1002/adfm.202112772
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Artificial intelligence for search and discovery of quantum materials
Communications Materials, 2(1), 105, 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), 190902, 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, 104437, 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), 2101122, 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, 10.35848/1347-4065/abbfa0
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Recent advances and future prospects in energy harvesting technologies
Japanese Journal of Applied Physics, 59(11), 110201, 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, 109168, 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), 2001537, 2020, 10.1002/adma.202001537
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Machine Learning Approaches for Thermoelectric Materials Research
Advanced Functional Materials, 2020, 10.1002/adfm.201906041