Properties optimisation of nanostructures via machine learning: Progress and perspective
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...
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| Format: | Article |
| Language: | English English |
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Elsevier Ltd
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/43160/ http://umpir.ump.edu.my/id/eprint/43160/1/Properties%20optimisation%20of%20nanostructures%20via%20machine%20learning_ABST.pdf http://umpir.ump.edu.my/id/eprint/43160/2/Properties%20optimisation%20of%20nanostructures%20via%20machine%20learning.pdf |
| _version_ | 1848826801707548672 |
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| author | Nurul Akmal, Che Lah |
| author_facet | Nurul Akmal, Che Lah |
| author_sort | Nurul Akmal, Che Lah |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T03:50:36Z |
| format | Article |
| id | ump-43160 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:50:36Z |
| publishDate | 2024 |
| publisher | Elsevier Ltd |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-431602024-12-17T01:13:08Z http://umpir.ump.edu.my/id/eprint/43160/ Properties optimisation of nanostructures via machine learning: Progress and perspective Nurul Akmal, Che Lah TJ Mechanical engineering and machinery TS Manufactures 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. Elsevier Ltd 2024-12-04 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/43160/1/Properties%20optimisation%20of%20nanostructures%20via%20machine%20learning_ABST.pdf pdf en http://umpir.ump.edu.my/id/eprint/43160/2/Properties%20optimisation%20of%20nanostructures%20via%20machine%20learning.pdf 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) https://doi.org/10.1016/j.enganabound.2024.106063 https://doi.org/10.1016/j.enganabound.2024.106063 |
| spellingShingle | TJ Mechanical engineering and machinery TS Manufactures Nurul Akmal, Che Lah Properties optimisation of nanostructures via machine learning: Progress and perspective |
| title | Properties optimisation of nanostructures via machine learning: Progress and perspective |
| title_full | Properties optimisation of nanostructures via machine learning: Progress and perspective |
| title_fullStr | Properties optimisation of nanostructures via machine learning: Progress and perspective |
| title_full_unstemmed | Properties optimisation of nanostructures via machine learning: Progress and perspective |
| title_short | Properties optimisation of nanostructures via machine learning: Progress and perspective |
| title_sort | properties optimisation of nanostructures via machine learning: progress and perspective |
| topic | TJ Mechanical engineering and machinery TS Manufactures |
| url | http://umpir.ump.edu.my/id/eprint/43160/ http://umpir.ump.edu.my/id/eprint/43160/ http://umpir.ump.edu.my/id/eprint/43160/ http://umpir.ump.edu.my/id/eprint/43160/1/Properties%20optimisation%20of%20nanostructures%20via%20machine%20learning_ABST.pdf http://umpir.ump.edu.my/id/eprint/43160/2/Properties%20optimisation%20of%20nanostructures%20via%20machine%20learning.pdf |