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RESEARCH PAPER · npj Computational Materials · 2018

Empirical modeling of dopability in diamond-like semiconductors

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42 direct citing works

Empirical modeling of dopability in diamond-like semiconductorsOriginalInertial effective mass as an effective descriptor for thermoelectrics via data-driven evaluation70III-Sb-based solar cells and their integration on Si0Machine Learning Approaches for Thermoelectric Materials Research231On the Dopability of Semiconductors and Governing Material Properties50Active learning for the power factor prediction in diamond-like thermoelectric materials87Controlling thermoelectric transport via native defects in the diamond-like semiconductors Cu 2 HgGeTe 4 and Hg 2 GeTe 410Machine Learning Regression Guided Thermoelectric Materials Discovery – A Review44Visualizing defect energetics16Sulfide Perovskites for Thermoelectricity37Electrical doping in halide perovskites364Phase Boundary Mapping of Tin‐Doped ZnSb Reveals Thermodynamic Route to High Thermoelectric Efficiency29Disorder-induced Anderson-like localization for bidimensional thermoelectrics optimization35Thermoelectric materials and transport physics215Growth defects of organic crystals: A review60Machine Learning Driven Synthesis of Few-Layered WTe2 with Geometrical Control68Recent advances in designing thermoelectric materials169Universal machine learning framework for defect predictions in zinc blende semiconductors56High-Throughput Screening for Thermoelectric Semiconductors with Desired Conduction Types by Energy Positions of Band Edges34Studying Complex Evolution of Hyperelastic Materials under External Field Stimuli using Artificial Neural Networks with Spatiotemporal Features in a Small‐Scale Dataset35A Convergent Understanding of Charged Defects29The centre cannot hold0Phase-Boundary Mapping to Engineer Defects in Thermoelectric Materials30Designing for dopability in semiconducting AgInTe 210Extrinsic doping of Hg 2 GeTe 4 in the face of defect compensation and phase competition2A Critical Review of Machine Learning Techniques on Thermoelectric Materials66Charting Ba-Based Double Perovskite Oxides for Visible-Light-Driven Photocatalytic Water Splitting23New Materials and Devices for Thermoelectric Power Generation6Using Machine Learning Techniques to Discover Novel Thermoelectric Materials4Computational and Data‐Driven Development of Thermoelectric Materials0Unlocking the potential of coinage-based quaternary chalcogenides for thermoelectricity16High‐Throughput Strategies in the Discovery of Thermoelectric Materials69Trends in GeTe Thermoelectrics: From Fundamentals to Applications61Design Principles Guided by DFT Calculations and High-Throughput Frameworks for the Discovery of New Diamond-like Chalcogenide Thermoelectric Materials14Amphoteric doping and thermoelectric transport in the CuInTe 2 –ZnTe solid solution4Bridging current and future innovations to unlock the potential of multifunctional materials for sustainable energy applications28Disorder by design: high-entropy oxides as next generation thermoelectric materials11Solid-State Molecular Architecture in ABTe3: Revealing Chemically Inspired Features for Thermoelectricity0Recent strides in artificial intelligence for predicting thermoelectric properties and materials discovery9Advancements in thermoelectric materials: Emerging trends in organic, inorganic systems, and material informatics12Machine learning for predictive design and optimization of high-performance thermoelectric materials: a review13A review of machine learning approaches for the discovery of thermoelectric materials3High throughput and machine learning approaches for thermoelectric materials4Searching for high-performance thermoelectric materials via an advanced machine learning framework0Topological thermoelectrics: analytical framework, material aspects and machine learning1A comprehensive review of thermoelectric power generators: Advances in material discovery, structural optimization for performance enhancement, and service life stability0Machine learning-accelerated discovery and optimization of thermoelectric materials for energy conversion0

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  2. III-Sb-based solar cells and their integration on Si

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  3. Machine Learning Approaches for Thermoelectric Materials Research

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  4. On the Dopability of Semiconductors and Governing Material Properties

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  5. Active learning for the power factor prediction in diamond-like thermoelectric materials

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  6. Controlling thermoelectric transport via native defects in the diamond-like semiconductors Cu 2 HgGeTe 4 and Hg 2 GeTe 4

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  10. Electrical doping in halide perovskites

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  11. Phase Boundary Mapping of Tin‐Doped ZnSb Reveals Thermodynamic Route to High Thermoelectric Efficiency

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  12. Disorder-induced Anderson-like localization for bidimensional thermoelectrics optimization

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    Matthias T. Agne, Felix Rolf Lutz Lange, James Patrick Male, Karl Simon Siegert, Hanno Volker, Christian Poltorak, Annika Poitz, Theo Siegrist, Stefan Maier, Gerald Jeffrey Snyder, Matthias Wuttig

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  14. Growth defects of organic crystals: A review

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  15. Machine Learning Driven Synthesis of Few-Layered WTe2 with Geometrical Control

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  18. High-Throughput Screening for Thermoelectric Semiconductors with Desired Conduction Types by Energy Positions of Band Edges

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  19. Studying Complex Evolution of Hyperelastic Materials under External Field Stimuli using Artificial Neural Networks with Spatiotemporal Features in a Small‐Scale Dataset

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  20. A Convergent Understanding of Charged Defects

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  21. The centre cannot hold

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  22. Phase-Boundary Mapping to Engineer Defects in Thermoelectric Materials

    PRX Energy · 2022-09-15

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  23. Designing for dopability in semiconducting AgInTe 2

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  24. Extrinsic doping of Hg 2 GeTe 4 in the face of defect compensation and phase competition

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  25. A Critical Review of Machine Learning Techniques on Thermoelectric Materials

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  26. Charting Ba-Based Double Perovskite Oxides for Visible-Light-Driven Photocatalytic Water Splitting

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  27. New Materials and Devices for Thermoelectric Power Generation

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