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The discovery of high-dielectric materials is crucial to increasing the efficiency of electronic devices and batteries. Here, we report three previously unexplored materials with very high dielectric constants (69 < ϵ < 101) and large band gaps (2.9 < E_g(eV) < 5.5) obtained by screening materials databases using statistical optimization algorithms aided by artificial neural networks (ANN). Two of these new dielectrics are mixed-anion compounds (Eu₅SiCl₆O₄and HoClO) and are shown to be thermodynamically stable against common semiconductors via phase diagram analysis. We also uncovered four other materials with relatively large dielectric constants (20 < ϵ < 40) and band gaps (2.3 < E_g(eV) < 2.7). While the ANN training-data are obtained from the Materials Project, the search-space consists of materials from the Open Quantum Materials Database (OQMD)—demonstrating a successful implementation of cross-database materials design. Overall, we report the dielectric properties of 17 materials calculated using ab initio calculations, that were selected in our design workflow. The dielectric materials with high-dielectric properties predicted in this work open up further experimental research opportunities.

 

Abstract Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science. 

The development of efficient thermal energy management devices such as thermoelectrics and barrier coatings often relies on compounds having low lattice thermal conductivity (κ_l). Here, we present the computational discovery of a large family of 628 thermodynamically stable quaternary chalcogenides, AMM′Q₃(A = alkali/alkaline earth/post-transition metals; M/M′ = transition metals, lanthanides; Q = chalcogens) using high-throughput density functional theory (DFT) calculations. We validate the presence of lowκ_lin these materials by calculatingκ_lof several predicted stable compounds using the Peierls–Boltzmann transport equation. Our analysis reveals that the lowκ_loriginates from the presence of either a strong lattice anharmonicity that enhances the phonon-scatterings or rattler cations that lead to multiple scattering channels in their crystal structures. Our thermoelectric calculations indicate that some of the predicted semiconductors may possess high energy conversion efficiency with their figure-of-merits exceeding 1 near 600 K. Our predictions suggest experimental research opportunities in the synthesis and characterization of these stable, lowκ_lcompounds.

 

Spaceflight presents a multifaceted environment for plants, combining the effects on growth of many stressors and factors including altered gravity, the influence of experiment hardware, and increased radiation exposure. To help understand the plant response to this complex suite of factors this study compared transcriptomic analysis of 15Arabidopsis thalianaspaceflight experiments deposited in the National Aeronautics and Space Administration’s GeneLab data repository. These data were reanalyzed for genes showing significant differential expression in spaceflight versus ground controls using a single common computational pipeline for either the microarray or the RNA-seq datasets. Such a standardized approach to analysis should greatly increase the robustness of comparisons made between datasets. This analysis was coupled with extensive cross-referencing to a curated matrix of metadata associated with these experiments. Our study reveals that factors such as analysis type (i.e., microarray versus RNA-seq) or environmental and hardware conditions have important confounding effects on comparisons seeking to define plant reactions to spaceflight. The metadata matrix allows selection of studies with high similarity scores, i.e., that share multiple elements of experimental design, such as plant age or flight hardware. Comparisons between these studies then helps reduce the complexity in drawing conclusions arising from comparisons made between experiments with very different designs.