guest ::
| Home > Publications database > Efficient surrogate models for materials science simulations: Machine learning-based prediction of microstructure properties > print |
| Home > Publications database > Efficient surrogate models for materials science simulations: Machine learning-based prediction of microstructure properties > print |
| 001 | 1025204 | ||
| 005 | 20250204113838.0 | ||
| 024 | 7 | _ | |a 10.1016/j.mlwa.2024.100544 |2 doi |
| 024 | 7 | _ | |a 10.34734/FZJ-2024-02772 |2 datacite_doi |
| 024 | 7 | _ | |a WOS:001289088000001 |2 WOS |
| 037 | _ | _ | |a FZJ-2024-02772 |
| 082 | _ | _ | |a 004 |
| 100 | 1 | _ | |a Nguyen, Binh Duong |0 P:(DE-Juel1)187067 |b 0 |u fzj |
| 245 | _ | _ | |a Efficient surrogate models for materials science simulations: Machine learning-based prediction of microstructure properties |
| 260 | _ | _ | |a Amsterdam |c 2024 |b Elsevier |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1714712913_30347 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a Determining, understanding, and predicting the so-called structure–property relation is an important task in many scientific disciplines, such as chemistry, biology, meteorology, physics, engineering, and materials science. Structure refers to the spatial distribution of, e.g., substances, material, or matter in general, while property is a resulting characteristic that usually depends in a non-trivial way on spatial details of the structure. Traditionally, forward simulations models have been used for such tasks. Recently, several machine learning algorithms have been applied in these scientific fields to enhance and accelerate simulation models or as surrogate models. In this work, we develop and investigate the applications of six machine learning techniques based on two different datasets from the domain of materials science: data from a two-dimensional Ising model for predicting the formation of magnetic domains and data representing the evolution of dual-phase microstructures from the Cahn–Hilliard model. We analyze the accuracy and robustness of all models and elucidate the reasons for the differences in their performances. The impact of including domain knowledge through tailored features is studied, and general recommendations based on the availability and quality of training data are derived from this. |
| 536 | _ | _ | |a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5111 |c POF4-511 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
| 700 | 1 | _ | |a Potapenko, Pavlo |0 P:(DE-Juel1)187058 |b 1 |u fzj |
| 700 | 1 | _ | |a Demirci, Aytekin |0 P:(DE-Juel1)186709 |b 2 |u fzj |
| 700 | 1 | _ | |a Govind, Kishan |0 P:(DE-Juel1)186834 |b 3 |u fzj |
| 700 | 1 | _ | |a Bompas, Sébastien |0 P:(DE-Juel1)190683 |b 4 |u fzj |
| 700 | 1 | _ | |a Sandfeld, Stefan |0 P:(DE-Juel1)186075 |b 5 |e Corresponding author |
| 773 | _ | _ | |a 10.1016/j.mlwa.2024.100544 |g Vol. 16, p. 100544 - |0 PERI:(DE-600)3052291-2 |p 100544 - |t Machine learning with applications |v 16 |y 2024 |x 2666-8270 |
| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/1025204/files/1-s2.0-S2666827024000203-main.pdf |
| 856 | 4 | _ | |y OpenAccess |x icon |u https://juser.fz-juelich.de/record/1025204/files/1-s2.0-S2666827024000203-main.gif?subformat=icon |
| 856 | 4 | _ | |y OpenAccess |x icon-1440 |u https://juser.fz-juelich.de/record/1025204/files/1-s2.0-S2666827024000203-main.jpg?subformat=icon-1440 |
| 856 | 4 | _ | |y OpenAccess |x icon-180 |u https://juser.fz-juelich.de/record/1025204/files/1-s2.0-S2666827024000203-main.jpg?subformat=icon-180 |
| 856 | 4 | _ | |y OpenAccess |x icon-640 |u https://juser.fz-juelich.de/record/1025204/files/1-s2.0-S2666827024000203-main.jpg?subformat=icon-640 |
| 909 | C | O | |o oai:juser.fz-juelich.de:1025204 |p openaire |p open_access |p OpenAPC |p driver |p VDB |p openCost |p dnbdelivery |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)187067 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-Juel1)187058 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)186709 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)186834 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 4 |6 P:(DE-Juel1)190683 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 5 |6 P:(DE-Juel1)186075 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5111 |x 0 |
| 914 | 1 | _ | |y 2024 |
| 915 | p | c | |a APC keys set |2 APC |0 PC:(DE-HGF)0000 |
| 915 | p | c | |a Local Funding |2 APC |0 PC:(DE-HGF)0001 |
| 915 | p | c | |a DFG OA Publikationskosten |2 APC |0 PC:(DE-HGF)0002 |
| 915 | p | c | |a DEAL: Elsevier 09/01/2023 |2 APC |0 PC:(DE-HGF)0125 |
| 915 | p | c | |a DOAJ Journal |2 APC |0 PC:(DE-HGF)0003 |
| 915 | _ | _ | |a Creative Commons Attribution-NonCommercial CC BY-NC 4.0 |0 LIC:(DE-HGF)CCBYNC4 |2 HGFVOC |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0501 |2 StatID |b DOAJ Seal |d 2021-01-29T11:25:19Z |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0500 |2 StatID |b DOAJ |d 2021-01-29T11:25:19Z |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b DOAJ : Anonymous peer review |d 2021-01-29T11:25:19Z |
| 915 | _ | _ | |a Article Processing Charges |0 StatID:(DE-HGF)0561 |2 StatID |d 2023-09-02 |
| 915 | _ | _ | |a Fees |0 StatID:(DE-HGF)0700 |2 StatID |d 2023-09-02 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2025-01-07 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2025-01-07 |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0112 |2 StatID |b Emerging Sources Citation Index |d 2025-01-07 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2025-01-07 |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-9-20201008 |k IAS-9 |l Materials Data Science and Informatics |x 0 |
| 980 | 1 | _ | |a FullTexts |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)IAS-9-20201008 |
| 980 | _ | _ | |a APC |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|