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.
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): | Creative Commons - CC BY-ND - Namensnennung - Keine Bearbeitungen 4.0 International |