Transforming Computational Nanotechnology: Accelerating Material Discovery, Design, and Property Prediction through Soft Computing Techniques

H Das and M Sharma, ACS APPLIED ELECTRONIC MATERIALS, 7, 5757-5787 (2025).

DOI: 10.1021/acsaelm.5c00842

The rapid growth of nanotechnology has enabled the design and synthesis of materials on the nanoscale, transforming various scientific fields. However, the complex behavior of nanostructures, coupled with challenges in material design and property prediction, limits its full potential. Traditional trial-and-error methods are often resource-intensive and inefficient, necessitating computational approaches to accelerate material discovery and optimization. Recently, soft computing techniques (SCTs) such as machine learning (ML), genetic algorithms (GAs), fuzzy logic (FL), and artificial neural networks (ANNs) have emerged as powerful tools to address these challenges. These data-driven frameworks enable accurate property prediction, synthesis optimization, and rapid identification of novel nanomaterials, reducing reliance on costly experiments. Hybrid approaches that integrate ML, GAs, and FL further enhance multiobjective optimization and predictive modeling, allowing researchers to explore vast design spaces efficiently. These integrated techniques have been successfully applied to nanophotonic devices, energy storage materials, and catalytic systems, demonstrating their ability to tackle long-standing challenges in nanoscience and nanotechnology. This review, focusing primarily on studies published in the last five years, explores the transformative role of SCTs in computational nanotechnology, highlighting key applications, challenges, and future directions. Additionally, issues such as data availability, model interpretability, and scalability are discussed along with potential solutions. By consolidating recent advancements, this work provides a comprehensive roadmap for leveraging SCTs in materials science, paving the way for the design and development of next- generation high-performance materials and functional nanostructures.

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