Authors: Dr Sheela Hundekari | Navin Prakash | Sudheer Choudari | Md Asaduzzaman | Bijoy Laxmi Koley | Anupam Kumar Biswas | Saheli Ray
Source: Nanotechnology Perceptions (article, 2024-11-04)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Nanomaterial Design: Predictive Modeling for Enhanced Performance in Electronics The integration of machine learning (ML) into nanomaterial design is transforming electronics by enabling predictive modeling for enhanced material properties and device performance. Nanomaterials, with their unique characteristics and extensive applications in semiconductors, batteries, and sensors, hold the key to the next generation of electronic advancements. However, optimizing nanomaterial properties requires navigating a vast parameter space, encompassing atomic composition, structural morphology, and functional characteristics, which conventional experimental approaches alone struggle to manage efficiently. This study leverages advanced ML techniques to address this complexity, offering a powerful framework for predictive material design tailored specifically for high-performance electronics. We propose a novel, data-driven methodology for nanomaterial property prediction, utilizing supervised learning models trained on large-scale datasets of nanomaterial compositions, fabrication parameters, and performance metrics. Our approach emphasizes model interpretability and accuracy by deploying a combination of neural networks, support vector machines, and ensemble techniques, which collectively capture nonlinear relationships within the data. The proposed ML models are capable of predicting critical material properties, such as conductivity, thermal stability, and electron mobility, with a high degree of precision. Moreover, to enhance model robustness, we incorporate feature engineering techniques, extracting meaningful descriptors from raw data, which allows for the identification of key structural and compositional factors impacting performance.A key focus of our research is the integration of transfer learning, enabling the reuse of knowledge across similar material classes and reducing the need for extensive labeled data, which is often scarce or expensive to acquire. The transfer learning models adapt to new nanomaterial types by building on pre-trained models, leading to faster convergence and more accurate predictions in new domains, such as emerging two-dimensional materials and nanocomposites. This approach not only reduces computational costs but also accelerates the discovery process for novel nanomaterials in the electronics sector. To validate our models, we conduct a series of experiments on nanomaterials used in transistors, memory devices, and flexible electronics. The performance of the predictive models is evaluated based on accuracy, generalizability, and computational efficiency. The findings demonstrate that ML-driven predictive modeling can achieve a substantial improvement in both the speed and accuracy of nanomaterial design compared to traditional trial-and-error approaches. Notably, the models reveal complex interactions between structural attributes and electronic properties, offering insights that guide experimental synthesis for enhanced functionality.The implications of this work extend beyond predictive accuracy; by reducing the experimental burden, this methodology accelerates the design cycle, enabling rapid prototyping and adaptation of nanomaterials to meet specific electronic performance demands. Additionally, the interpretability of ML models provides a transparent link between nanomaterial attributes and device performance, bridging the gap between computational predictions and experimental realization. This study not only presents a robust framework for data-driven nanomaterial design but also establishes a foundation for future research in the application of ML to complex material systems. Therefore, machine learning offers a promising path forward in the design of high-performance nanomaterials for electronics, where predictive modeling can streamline discovery and improve material outcomes. Our study contributes to the growing field of ML-driven materials science by introducing models that are both accurate and scalable, paving the way for intelligent nanomaterial design in next-generation electronic devices. Interpretability ; Robustness (evolution) ; Computer science ; Machine learning ; Artificial intelligence ; Electronics ; Engineering Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Advanced Memory and Neural Computing