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Machine Learning in the development of Si-based anodes using Small-Angle X-ray Scattering for structural property analysis

  • Material development processes are highly iterative and driven by the experience and intuition of the researcher. This can lead to time consuming procedures. Data-driven approaches such as Machine Learning can support decision processes with trained and validated models to predict certain output parameter. In a multifaceted process chain of material synthesis of electrochemical materials and their characterization, Machine Learning has a huge potential to shorten development processes. Based on this, the contribution presents a novel approach to utilize data derived from Small-Angle X-ray Scattering (SAXS) of SiO_2 matrix materials for battery anodes with Neural Networks. Here, we use SAXS as an intermediate, high-throughput method to characterize sol–gel based porous materials. A multi-step-method is presented where a Feed Forward Net is connected to a pretrained autoencoder to reliably map parameters of the material synthesis to the SAXS curve of the resulting material. In addition, a direct comparison shows that the prediction error of Neural Networks can be greatly reduced by training each output variable with a separate independent Neural Network.

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Metadaten
Author:Jan Schmitt, Philipp Seitz, Christian Scherdel, Gudrun Reichenauer
Persistent identifier:https://doi.org/10.1016/j.commatsci.2022.111984
ISSN:1879-0801
Parent Title (English):Computational Materials Science
Document Type:Article
Language:English
Date of first Publication:2023/02/22
Publishing Institution:Hochschule für Angewandte Wissenschaften Würzburg-Schweinfurt
Release Date:2023/02/22
Tag:autoencoder; machine learning; neural network
Volume:218
Comments:
Link zum Datensatz: https://gitlab.vlab.fm.fhws.de/philipp.seitz/machinelearningandsaxs
Institutes and faculty:Fakultäten / Fakultät Wirtschaftsingenieurwesen
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 006 Spezielle Computerverfahren
Regensburger Klassifikation:Informatik
Open access colour:Gold
Licence (German):License LogoCreative Commons - CC BY-ND - Namensnennung - Keine Bearbeitungen 4.0 International
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