Matches in SemOpenAlex for { <https://semopenalex.org/work/W3208687975> ?p ?o ?g. }
- W3208687975 abstract "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." @default.
- W3208687975 created "2021-11-08" @default.
- W3208687975 creator A5002723657 @default.
- W3208687975 creator A5003640520 @default.
- W3208687975 creator A5004659592 @default.
- W3208687975 creator A5016287426 @default.
- W3208687975 creator A5019215236 @default.
- W3208687975 creator A5022871406 @default.
- W3208687975 creator A5053080365 @default.
- W3208687975 creator A5065794019 @default.
- W3208687975 creator A5066580911 @default.
- W3208687975 creator A5074976770 @default.
- W3208687975 creator A5078422186 @default.
- W3208687975 creator A5087949669 @default.
- W3208687975 creator A5088846466 @default.
- W3208687975 date "2022-04-05" @default.
- W3208687975 modified "2023-10-18" @default.
- W3208687975 title "Recent advances and applications of deep learning methods in materials science" @default.
- W3208687975 cites W1480171480 @default.
- W3208687975 cites W1502922572 @default.
- W3208687975 cites W1966000114 @default.
- W3208687975 cites W1975997599 @default.
- W3208687975 cites W1987731163 @default.
- W3208687975 cites W1992985800 @default.
- W3208687975 cites W1993046136 @default.
- W3208687975 cites W1995341919 @default.
- W3208687975 cites W1999145583 @default.
- W3208687975 cites W1999925413 @default.
- W3208687975 cites W2008505552 @default.
- W3208687975 cites W2017447741 @default.
- W3208687975 cites W2023972500 @default.
- W3208687975 cites W2025444507 @default.
- W3208687975 cites W2027482274 @default.
- W3208687975 cites W2029413789 @default.
- W3208687975 cites W2033428036 @default.
- W3208687975 cites W2040870580 @default.
- W3208687975 cites W2042640655 @default.
- W3208687975 cites W2046589863 @default.
- W3208687975 cites W2069194786 @default.
- W3208687975 cites W2080635178 @default.
- W3208687975 cites W2094868831 @default.
- W3208687975 cites W2099153308 @default.
- W3208687975 cites W2101553882 @default.
- W3208687975 cites W2103496339 @default.
- W3208687975 cites W2104489082 @default.
- W3208687975 cites W2106612397 @default.
- W3208687975 cites W2111044246 @default.
- W3208687975 cites W2112845989 @default.
- W3208687975 cites W2122025333 @default.
- W3208687975 cites W2122735672 @default.
- W3208687975 cites W2128629135 @default.
- W3208687975 cites W2134329894 @default.
- W3208687975 cites W2135662825 @default.
- W3208687975 cites W2137841449 @default.
- W3208687975 cites W2145992718 @default.
- W3208687975 cites W2154677248 @default.
- W3208687975 cites W2165671627 @default.
- W3208687975 cites W2169491861 @default.
- W3208687975 cites W2169544320 @default.
- W3208687975 cites W2169840733 @default.
- W3208687975 cites W2170214641 @default.
- W3208687975 cites W2200589053 @default.
- W3208687975 cites W2234529989 @default.
- W3208687975 cites W2252849902 @default.
- W3208687975 cites W2261254692 @default.
- W3208687975 cites W2278970271 @default.
- W3208687975 cites W2290847742 @default.
- W3208687975 cites W2302501749 @default.
- W3208687975 cites W2313966941 @default.
- W3208687975 cites W2338402873 @default.
- W3208687975 cites W2410722695 @default.
- W3208687975 cites W2464725281 @default.
- W3208687975 cites W2494566811 @default.
- W3208687975 cites W2509907061 @default.
- W3208687975 cites W2523785361 @default.
- W3208687975 cites W2526943155 @default.
- W3208687975 cites W2527540728 @default.
- W3208687975 cites W2536843706 @default.
- W3208687975 cites W2541404351 @default.
- W3208687975 cites W2564339002 @default.
- W3208687975 cites W2567575208 @default.
- W3208687975 cites W2583795764 @default.
- W3208687975 cites W2585152223 @default.
- W3208687975 cites W2585666583 @default.
- W3208687975 cites W2586460992 @default.
- W3208687975 cites W2594183968 @default.
- W3208687975 cites W2606850798 @default.
- W3208687975 cites W2610148085 @default.
- W3208687975 cites W2620045026 @default.
- W3208687975 cites W2625386340 @default.
- W3208687975 cites W2734520197 @default.
- W3208687975 cites W2740407088 @default.
- W3208687975 cites W2741056065 @default.
- W3208687975 cites W2742127985 @default.
- W3208687975 cites W2752532133 @default.
- W3208687975 cites W2753962198 @default.
- W3208687975 cites W2757455114 @default.
- W3208687975 cites W2757533756 @default.
- W3208687975 cites W2760202681 @default.
- W3208687975 cites W2763223203 @default.