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  Towards efficient and accurate input for data-driven materials science from large-scale all-electron density functional theory (DFT) simulations

Kokott, S., Marek, A., Merz, F., Karpov, P., Carbogno, C., Rossi, M., Rampp, M., Blum, V., & Scheffler, M. (2024). Towards efficient and accurate input for data-driven materials science from large-scale all-electron density functional theory (DFT) simulations. Modelling and Simulation in Materials Science and Engineering, 32(6), 28-31. doi:10.1088/1361-651X/ad4d0d.

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基本情報

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アイテムのパーマリンク: https://hdl.handle.net/21.11116/0000-000F-937C-0 版のパーマリンク: https://hdl.handle.net/21.11116/0000-000F-94A9-B
資料種別: 学術論文

ファイル

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:
Bauer_2024_Modelling_Simul._Mater._Sci._Eng._32_063301.pdf (出版社版), 7MB
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ファイルのパーマリンク:
https://hdl.handle.net/21.11116/0000-000F-937E-E
ファイル名:
Bauer_2024_Modelling_Simul._Mater._Sci._Eng._32_063301.pdf
説明:
"Roadmap on data-centric materials science", of which this article is a chapter
OA-Status:
Hybrid
閲覧制限:
公開
MIMEタイプ / チェックサム:
application/pdf / [MD5]
技術的なメタデータ:
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著作権日付:
2024
著作権情報:
© The Author(s). Published by IOP Publishing Ltd
CCライセンス:
https://creativecommons.org/licenses/by/4.0/

関連URL

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URL:
https://arxiv.org/abs/2402.10932 (プレプリント)
説明:
"Roadmap on data-centric materials science", of which this article is a chapter
OA-Status:
Not specified
URL:
https://doi.org/10.1088/1361-651X/ad4d0d (出版社版)
説明:
"Roadmap on data-centric materials science", of which this article is a chapter
OA-Status:
Hybrid

作成者

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 作成者:
Kokott, S.1, 2, 著者
Marek, A.3, 著者
Merz, F.4, 著者
Karpov, P.3, 著者
Carbogno, C.1, 著者
Rossi, M.5, 著者                 
Rampp, M.3, 著者
Blum, V.6, 著者
Scheffler, M.1, 著者
所属:
1The NOMAD Laboratory at the Fritz Haber Institute of the Max Planck Society, ou_persistent22              
2Molecular Simulations from First Principles e.V., ou_persistent22              
3Max Planck Computing and Data Facility, ou_persistent22              
4Lenovo HPC Innovation Center, ou_persistent22              
5Simulations from Ab Initio Approaches, Theory Department, Max Planck Institute for the Structure and Dynamics of Matter, Max Planck Society, ou_3185035              
6Thomas Lord Department of Mechanical Engineering and Materials Science, Duke University, ou_persistent22              

内容説明

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キーワード: -
 要旨: Science is and always has been based on data, but the terms 'data-centric' and the '4th paradigm' of materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of artificial intelligence and its subset machine learning, has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research.

資料詳細

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言語: eng - English
 日付: 2024-05-012024-01-242024-05-172024-07-032024-09
 出版の状態: 出版
 ページ: 4
 出版情報: -
 目次: -
 査読: 査読あり
 識別子（DOI, ISBNなど）: arXiv: 2402.10932
DOI: 10.1088/1361-651X/ad4d0d
 学位: -

関連イベント

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訴訟

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Project information

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Project name : -
Grant ID : 951786
Funding program : Horizon 2020 (H2020)
Funding organization : European Commission (EC)
Project name : This project received financial support from BiGmax, the Max Planck Society’s Research Network on Big-Data-Driven Materials Science, the NOMAD Center of Excellence (European Union’s Horizon 2020 research and innovation program, Grant Agreement No. 951786) and the ERC Advanced Grant TEC1p (European Research Council, Grant Agreement No. 740233).
Grant ID : -
Funding program : -
Funding organization : -

出版物 1

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出版物名: Modelling and Simulation in Materials Science and Engineering
  省略形 : Modelling Simul. Mater. Sci. Eng.
種別: 学術雑誌
 著者・編者:
所属:
出版社, 出版地: London : IOP Pub.
ページ: - 巻号: 32 (6) 通巻号: - 開始・終了ページ: 28 - 31 識別子（ISBN, ISSN, DOIなど）: ISSN: 0965-0393
CoNE: https://pure.mpg.de/cone/journals/resource/954925581155

出版物 2

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出版物名: Roadmap on data-centric materials science
種別: 論文集
 著者・編者:
Bauer, S.1, 著者
所属:
1 School of Computation, Information and Technology, Technical University of Munich & Helmholtz AI, ou_persistent22            
出版社, 出版地: -
ページ: - 巻号: - 通巻号: - 開始・終了ページ: - 識別子（ISBN, ISSN, DOIなど）: -