Predictive Modeling of Material Properties Using Nanotechnology and Artificial Intelligence Techniques
M Anupama,
Santhosh G,
G Vidhya,
M Suchithra
Accurate prediction of material properties is essential for accelerating the development of advanced
engineering materials, particularly those incorporating nanoscale features. Traditional experimental
and physics-based modeling approaches, while reliable, are often limited by high computational cost
and the complexity of capturing nonlinear nanoscale interactions. This study presents an integrated
predictive modeling framework that combines nanotechnology-derived material descriptors with
artificial intelligence techniques to estimate key material properties. Nanoscale parameters such as
particle size, volume fraction, and structural characteristics are used as inputs to train machine learning
models capable of learning complex structure–property relationships. The performance of AI-based
models is evaluated and compared with conventional empirical and physics-based approaches. Results
demonstrate that the artificial intelligence–driven framework achieves higher prediction accuracy and
significantly reduced computational effort while maintaining consistency with experimental observations.
The proposed approach highlights the potential of combining nanotechnology and artificial intelligence
to support efficient material design and optimization in advanced engineering applications.
