| Title |
Machine learning applications in the mechanical analysis of nanomaterials and nanostructures |
| Authors |
Sadeghian, Mostafa ; Palevicius, Arvydas ; Griskevicius, Paulius ; Janusas, Giedrius |
| DOI |
10.3390/app16020918 |
| Full Text |
|
| Is Part of |
Applied sciences.. Basel : MDPI. 2026, vol. 16, iss. 2, art. no. 918, p. 1-31.. ISSN 2076-3417 |
| Keywords [eng] |
machine learning ; mechanical analysis ; nanomaterials ; nanostructures |
| Abstract [eng] |
Machine learning (ML) is increasingly used to address the computational complexity and multiscale nature of mechanical analysis in nanomaterials and nanostructures. Traditional analytical, numerical, and atomistic approaches, such as continuum mechanics, finite element methods, and molecular dynamics (MD), often suffer from high computational cost or limited scalability when applied to nanoscale systems. Recently, ML techniques have been increasingly used to predict mechanical properties, analyze static and dynamic responses, and solve governing equations of nanostructures to improve efficiency and accuracy. This review provides a comprehensive overview of ML applications in the mechanical analysis of nanomaterials and nanostructures, including mechanical property prediction, static response analysis, and vibration analysis. Various ML techniques based on the property or type of the mechanical problem are discussed in detail. The review highlights current trends and provides structured guidance for future research on reliable and physically consistent ML methods for nanoscale mechanical analysis. |
| Published |
Basel : MDPI |
| Type |
Journal article |
| Language |
English |
| Publication date |
2026 |
| CC license |
|
