Properties optimisation of nanostructures via machine learning: Progress and perspective

Nurul Akmal, Che Lah (2024) Properties optimisation of nanostructures via machine learning: Progress and perspective. Engineering Analysis with Boundary Elements, 171 (106063). pp. 1-15. ISSN 0955-7997. (Published)

Preview	Pdf Properties optimisation of nanostructures via machine learning_ABST.pdf Download (247kB) | Preview
	Pdf Properties optimisation of nanostructures via machine learning.pdf Restricted to Repository staff only Download (14MB) | Request a copy

Abstract

Nanostructures play a vast role in the current Internet of NanoThings (IoNT) era due to remarkable properties and features that precisely impart their desired application functions in catalysis, energy and other fields. The exploration in understanding their minute features caused by the flexibility of compositional and complex atomic arrangement from the synthesis reaction widely opens for the in-depth discovery of their search space such as particle size, morphology and structures that controlled the characteristics. A wide range of possible compositions and various lattice atomic arrangements combined with small particle size distribution and large surface area create grand challenges to copy/differentiate their corresponding specific properties. Thus, the employment of machine learning (ML)-based strategies using the closed-loop experimental data from the nanostructure synthesis to help navigate and optimise for the large classes of data attributes related to the size, morphology and other properties from the trained model are reviewed. The data attributes are assisted by discussions of the selected case studies from the recent literature that highlight different condition nanostructures. The review concludes with a discussion of perspectives on the major challenges in the implementation of ML data-driven design in the field of nanostructure synthesis.

Item Type:	Article
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Deep-learning; Machine-learning; Morphology; Nanostructures; Particle size
Subjects:	T Technology > TJ Mechanical engineering and machinery T Technology > TS Manufactures
Faculty/Division:	Centre for Advanced Intelligent Materials Faculty of Manufacturing and Mechatronic Engineering Technology
Depositing User:	Mrs Norsaini Abdul Samat
Date Deposited:	17 Dec 2024 01:13
Last Modified:	17 Dec 2024 01:13
URI:	http://umpir.ump.edu.my/id/eprint/43160
Download Statistic:	View Download Statistics

Actions (login required)

View Item