Discovery and prediction capabilities in metal-based nanomaterials: An overview of the application of machine learning techniques and some recent advances. (April 2022)

• Record Type: Journal Article
• Authors: Anuoluwa Bamidele, Emmanuel; Olanrewaju Ijaola, Ahmed; Bodunrin, Michael; Ajiteru, Oluwaniyi; Martha Oyibo, Afure; Makhatha, Elizabeth; Asmatulu, Eylem
• Is Part Of: Advanced engineering informatics. Volume 52(2022)
• Publication Date: 2022-04
• Subjects: Machine Learning -- Metal-based nanomaterials -- Nanoinformatics -- Computational Materials -- Nanotechnology -- Inorganic nanoparticles; ANN Artificial Neural Network -- AFM Atomic Force Microscopy -- CD Circular Dichroism -- CNN Convolution Neural Network -- DNN Deep Neural Network -- DFT Density Function Theory -- DLS Dynamic Light Scattering -- EDS Energy-Dispersive X-Ray Spectroscopy -- FDTD Finite-Difference Time-Domain -- FCS Fluorescence Correlation Spectroscopy -- FTIR Fourier Transform Infrared Spectroscopy -- GPR Gaussian Process Regression -- GB Gradient Boosting -- GDA Generalized Discriminant Analysis -- ICP-MS Induced Coupled Plasma-Mass Spectrometry -- KNN K-Nearest Neighbor -- LASSO Least Absolute Shrinkage And Selection Operator -- LDA Linear Discriminant Analysis -- ML Machine Learning -- MS Mass Spectroscopy -- MD Molecular Dynamics. -- NLP Natural Language Processing -- NMR Nuclear Magnetic Resonance -- PCA Principal Component Analysis -- RF Random Forests -- RBS Rutherford Backscattering Spectrometry -- RMSE Root Mean Squared Error -- SEM Scanning Electron Microscopy -- SPM Scanning Probe Microscopy -- STM Scanning Tunneling Microscopy -- SAXS Small-Angle X-Ray Scattering -- SVM Support Vector Machine -- SVR Support Vector Regression -- SERS Surface-Enhanced Raman Spectroscopy -- TNA Thermal Neutron Analysis -- TERS Tip-Enhanced Raman Spectroscopy -- TEM Transmission Electron Microscopy -- UV–vis Ultraviolet Visible -- XRD X-Ray Diffraction -- XPS X-Ray Photoelectron Spectroscopy; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2022.101593
• Languages: English
• ISSNs: 1474-0346

Abstract

Graphical abstract: Abstract: The application of machine learning (ML) techniques to metal-based nanomaterials has contributed greatly to understanding the interaction of nanoparticles, properties prediction, and new materials discovery. However, the prediction accuracy and efficiency of distinctive ML algorithms differ with different metal-based nanomaterials problems. This, alongside the high dimensionality and nonlinearity of available datasets in metal-based nanomaterials problems, makes it imperative to review recent advances in the implementation of ML techniques for these kinds of problems. In addition to understanding the applicability of different ML algorithms to various kinds of metal-based nanomaterials problems, it is hoped that this work will help facilitate understanding and promote interest in this emerging and less explored area of materials informatics. The scope of this review covers the introduction of metal-based nanomaterials, several techniques used in generating datasets for training ML models, feature engineering techniques used in nanomaterials-machine learning applications, and commonly applied ML algorithms. Then, we present the recent advances in ML applications to metal-based nanomaterials, with emphasis on the procedure and efficiency of algorithms used for such applications. In the concluding section, we identify the most common and efficient algorithms for distinctive property predictions. The common problems encountered in ML applications for metal-based nanoinformatics were mentioned. Finally, we propose suitable solutions and future outlooks for various challenges in metal-based nanoinformatics research.