| 1. |
- Ghiringhelli, Luca M., et al.
(författare)
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Shared metadata for data-centric materials science
- 2023
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Ingår i: Scientific Data. - : NATURE PORTFOLIO. - 2052-4463. ; 10
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- The expansive production of data in materials science, their widespread sharing and repurposing requires educated support and stewardship. In order to ensure that this need helps rather than hinders scientific work, the implementation of the FAIR-data principles (Findable, Accessible, Interoperable, and Reusable) must not be too narrow. Besides, the wider materials-science community ought to agree on the strategies to tackle the challenges that are specific to its data, both from computations and experiments. In this paper, we present the result of the discussions held at the workshop on “Shared Metadata and Data Formats for Big-Data Driven Materials Science”. We start from an operative definition of metadata, and the features that a FAIR-compliant metadata schema should have. We will mainly focus on computational materials-science data and propose a constructive approach for the FAIRification of the (meta)data related to ground-state and excited-states calculations, potential-energy sampling, and generalized workflows. Finally, challenges with the FAIRification of experimental (meta)data and materials-science ontologies are presented together with an outlook of how to meet them.
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| 2. |
- Knoop, Florian, et al.
(författare)
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Ab initio Green-Kubo simulations of heat transport in solids: Method and implementation
- 2023
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Ingår i: Physical Review B. - : American Physical Society. - 2469-9950 .- 2469-9969. ; 107:22
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Tidskriftsartikel (refereegranskat)abstract
- Ab initio Green-Kubo (aiGK) simulations of heat transport in solids allow for assessing lattice thermalconductivity in anharmonic or complex materials from first principles. In this work, we present a detailed accountof their practical application and evaluation with an emphasis on noise reduction and finite-size corrections insemiconductors and insulators. To account for such corrections, we propose strategies in which all necessarynumerical parameters are chosen based on the dynamical properties displayed during molecular dynamicssimulations in order to minimize manual intervention. This paves the way for applying the aiGK method insemiautomated and high-throughput frameworks. The proposed strategies are presented and demonstrated forcomputing the lattice thermal conductivity at room temperature in the mildly anharmonic periclase MgO, andfor the strongly anharmonic marshite CuI.
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| 3. |
- Knoop, Florian, et al.
(författare)
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Anharmonicity in Thermal Insulators: An Analysis from First Principles
- 2023
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Ingår i: Physical Review Letters. - : AMER PHYSICAL SOC. - 0031-9007 .- 1079-7114. ; 130:23
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Tidskriftsartikel (refereegranskat)abstract
- The anharmonicity of atomic motion limits the thermal conductivity in crystalline solids. However, amicroscopic understanding of the mechanisms active in strong thermal insulators is lacking. In this Letter,we classify 465 experimentally known materials with respect to their anharmonicity and perform fullyanharmonic ab initio Green-Kubo calculations for 58 of them, finding 28 thermal insulators withκ < 10 W=mK including 6 with ultralow κ ≲ 1 W=mK. Our analysis reveals that the underlying stronganharmonic dynamics is driven by the exploration of metastable intrinsic defect geometries. This is atvariance with the frequently applied perturbative approach, in which the dynamics is assumed to evolvearound a single stable geometry.
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| 4. |
- Xian, R. Patrick, et al.
(författare)
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A machine learning route between band mapping and band structure
- 2023
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Ingår i: Nature Computational Science. - : Springer Nature. - 2662-8457. ; 3:1, s. 101-114
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Tidskriftsartikel (refereegranskat)abstract
- The electronic band structure and crystal structure are the two complementary identifiers of solid-state materials. Although convenient instruments and reconstruction algorithms have made large, empirical, crystal structure databases possible, extracting the quasiparticle dispersion (closely related to band structure) from photoemission band mapping data is currently limited by the available computational methods. To cope with the growing size and scale of photoemission data, here we develop a pipeline including probabilistic machine learning and the associated data processing, optimization and evaluation methods for band-structure reconstruction, leveraging theoretical calculations. The pipeline reconstructs all 14 valence bands of a semiconductor and shows excellent performance on benchmarks and other materials datasets. The reconstruction uncovers previously inaccessible momentum-space structural information on both global and local scales, while realizing a path towards integration with materials science databases. Our approach illustrates the potential of combining machine learning and domain knowledge for scalable feature extraction in multidimensional data.
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