Application of back propagation neural networks and random forest algorithms in material research of hydrogels

Abstract: In the fourth paradigm of science, which is data‐driven discovery, the big data collected from the first three paradigms can be analyzed to gain information of the research object. Especially in the field of material science, various big data‐driven methods are applied in the tasks, such as material detection, material analysis and material design. In the current research, we focus on how data‐driven methods, e.g., machine learning algorithms, play a big role in deciphering processing‐properties‐performance (PPP) relationships in hydrogels. We present the procedure of (i) normalization of hydrogel properties, (ii) feature engineering of hydrogels, which is to summarize the decisive features in each PPP section of hydrogels, and (iii) database building by data extraction from scientific literature of hydrogels. Finally, we select the two most promising machine learning algorithms, back propagation neural network and random forest algorithm. The back propagation neural network can contribute to prediction of hydrogels properties and the random forest algorithm can be applied to obtain deeper understanding of hydrogels in the early stage of the research.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Application of back propagation neural networks and random forest algorithms in material research of hydrogels ; volume:23 ; number:1 ; year:2023 ; extent:6 ; Proceedings in applied mathematics and mechanics ; 23, Heft 1 (2023) (gesamt 6)

Contributors, Places and Time

• Creator: Wang, Yawen ; Wallmersperger, Thomas ; Ehrenhofer, Adrian

Further information

• DOI: 10.1002/pamm.202200278
• URN: urn:nbn:de:101:1-2023060115135228512319
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 14.08.2025, 10:47 AM CEST