A systematic modeling methodology of deep neural network‐based structure‐property relationship for rapid and reliable prediction on flashpoints. Issue 1 (27th August 2021)

• Record Type: Journal Article
• Authors: Wen, Huaqiang; Su, Yang; Wang, Zihao; Jin, Saimeng; Ren, Jingzheng; Shen, Weifeng; Eden, Mario
• Is Part Of: AIChE journal. Volume 68:Issue 1(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-08-27
• Subjects: deep neural network -- flashpoint -- principal component analysis -- QSPR -- uncertainty analysis; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; 660.28
• DOI: 10.1002/aic.17402
• Languages: English
• ISSNs: 0001-1541

Abstract

Abstract: Deep neural networks (DNNs) based quantitative structure–property relationship (QSPR) studies are receiving increasing attention due to their excellent performances. A systematic methodology coupling multiple machine learning technologies is proposed to systematically solve vital problems including applicability domain and prediction uncertainty in DNN‐based QSPR modeling. Key features are rapidly extracted from plentiful but chaotic descriptors by principal component analysis (PCA) and kernel PCA. Then, a detailed applicability domain (AD) is defined by K‐means algorithm to avoid unreliable predictions and discover its potential impact on prediction uncertainty. Moreover, prediction uncertainty is analyzed with dropout ‐embedded DNN by thousands of independent tests to assess the reliability of predictions. The prediction of flashpoint temperature is employed as a case study, demonstrating that the model accuracy is remarkably improved comparing with the referenced model. Furthermore, the proposed methodology breaks through difficulties in analyzing the uncertainty of DNN‐based QSPRs and presents an AD correlated with the uncertainty.