eprintid: 33136
rev_number: 18
eprint_status: archive
userid: 5787
dir: disk0/00/03/31/36
datestamp: 2023-04-14 14:12:59
lastmod: 2023-04-17 07:33:38
status_changed: 2023-04-14 14:12:59
type: conferenceObject
metadata_visibility: show
creators_name: Tosato, Giovanna
creators_name: Koeppe, Arnd
creators_name: Selzer, Michael
creators_name: Nestler, Britta
corp_creators: Institute for Applied Materials – Microstructure Modelling and Simulation (IAM-MMS), Karlsruhe Institute of Technology (KIT),
corp_creators: Institute for Digital Materials Science (IDM), Karlsruhe University of Applied Science
title: Bayesian optimization framework for data-driven materials design
divisions: i-704000
pres_type: poster
abstract: The improvement of experimental design and the optimization of materials’properties with complex and partially unknown behaviors are common problems in material science. In the context of aqueous foams, the microstructure has a major influence on the properties of the resulting foam. Multiple interlinked parameters yield a large design space that requires tuning to tailor the microstructure evolution and resulting physical qualities. Our goal is a data-driven framework that uses machine learning to guide both experiments and simulations in an autonomous closed-loop. This iterative approach presents a valuable opportunity to accelerate materials development processes. A design of experiments methodology utilizing Bayesian Optimization is used to efficiently explore and exploit the search space, while minimizing the number of required evaluations. This approach allows to select the next most informative evaluation to perform, autonomously and adaptively learning from the already acquired data. The designed workflow is implemented into the data platform Kadi4Mat1, which provides the possibility of storing heterogeneous provenance data, along with a common workspace to integrate analysis methods and visualization. Our contribution within Kadi4Mat strongly relies on the reuse of data, and it is an example of the close interoperability between experimental and simulation research that the platform supports, in full alignment with the FAIR principles. Acknowledgements: This work is funded by the Ministry of Science, Research and Art Baden-Württemberg (MWK-BW) in the project MoMaF–Science Data Center, with funds from the state digitization strategy digital@bw (project number 57).
date: 2023
id_scheme: DOI
id_number: 10.11588/heidok.00033136
collection: c-61
ppn_swb: 1842773801
own_urn: urn:nbn:de:bsz:16-heidok-331366
language: eng
bibsort: BAYESIANOP20230302
full_text_status: public
place_of_pub: Heidelberg
pages: 1
event_title: E-Science-Tage 2023: Empower Your Research – Preserve Your Data
event_location: Heidelberg
event_dates: 01.03. - 03.03.2023
citation:   Tosato, Giovanna ; Koeppe, Arnd ; Selzer, Michael ; Nestler, Britta  (2023) Bayesian optimization framework for data-driven materials design.  [Conference Item]     
document_url: https://archiv.ub.uni-heidelberg.de/volltextserver/33136/7/Bayesian_optimization_E-Science-Tage_2023.pdf