Long, Teng ; Fortunato, Nuno M. ; Opahle, Ingo ; Zhang, Yixuan ; Samathrakis, Ilias ; Shen, Chen ; Gutfleisch, Oliver ; Zhang, Hongbin (2021)
Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures.
In: npj Computational Materials, 7 (66)
doi: 10.1038/s41524-021-00526-4
Artikel, Bibliographie
Dies ist die neueste Version dieses Eintrags.
Kurzbeschreibung (Abstract)
Autonomous materials discovery with desired properties is one of the ultimate goals for materials science, and the current studies have been focusing mostly on high-throughput screening based on density functional theory calculations and forward modeling of physical properties using machine learning. Applying the deep learning techniques, we have developed a generative model, which can predict distinct stable crystal structures by optimizing the formation energy in the latent space. It is demonstrated that the optimization of physical properties can be integrated into the generative model as on-top screening or backward propagator, both with their own advantages. Applying the generative models on the binary Bi-Se system reveals that distinct crystal structures can be obtained covering the whole composition range, and the phases on the convex hull can be reproduced after the generated structures are fully relaxed to the equilibrium. The method can be extended to multicomponent systems for multi-objective optimization, which paves the way to achieve the inverse design of materials with optimal properties.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Long, Teng ; Fortunato, Nuno M. ; Opahle, Ingo ; Zhang, Yixuan ; Samathrakis, Ilias ; Shen, Chen ; Gutfleisch, Oliver ; Zhang, Hongbin |
| Art des Eintrags: | Bibliographie |
| Titel: | Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures |
| Sprache: | Englisch |
| Publikationsjahr: | 2021 |
| Ort: | London |
| Verlag: | Springer Nature |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | npj Computational Materials |
| Jahrgang/Volume einer Zeitschrift: | 7 |
| (Heft-)Nummer: | 66 |
| Kollation: | 7 Seiten |
| DOI: | 10.1038/s41524-021-00526-4 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | Autonomous materials discovery with desired properties is one of the ultimate goals for materials science, and the current studies have been focusing mostly on high-throughput screening based on density functional theory calculations and forward modeling of physical properties using machine learning. Applying the deep learning techniques, we have developed a generative model, which can predict distinct stable crystal structures by optimizing the formation energy in the latent space. It is demonstrated that the optimization of physical properties can be integrated into the generative model as on-top screening or backward propagator, both with their own advantages. Applying the generative models on the binary Bi-Se system reveals that distinct crystal structures can be obtained covering the whole composition range, and the phases on the convex hull can be reproduced after the generated structures are fully relaxed to the equilibrium. The method can be extended to multicomponent systems for multi-objective optimization, which paves the way to achieve the inverse design of materials with optimal properties. |
| ID-Nummer: | Artikel-ID: 66 |
| Zusätzliche Informationen: | Supported by China Scholarship Council (CSC), the European Research Council (ERC) under the EU Horizon 2020 research and innovation programme (Grant No. 743116-project Cool Innov), DFG, German Research Foundation – Project-ID 405553726 – TRR 270. Publication was supported by Open Access Publishing Fund of TU Darmstadt. |
| Fachbereich(e)/-gebiet(e): | 11 Fachbereich Material- und Geowissenschaften 11 Fachbereich Material- und Geowissenschaften > Materialwissenschaft 11 Fachbereich Material- und Geowissenschaften > Materialwissenschaft > Fachgebiet Theorie magnetischer Materialien |
| Hinterlegungsdatum: | 20 Jan 2022 07:08 |
| Letzte Änderung: | 04 Okt 2024 08:29 |
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| Suche nach Titel in: | TUfind oder in Google |
Verfügbare Versionen dieses Eintrags
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Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures. (deposited 30 Sep 2024 08:23)
- Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures. (deposited 20 Jan 2022 07:08) [Gegenwärtig angezeigt]
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