AI-Driven Sustainable Materials Discovery for Next-Generation Engineering Applications

  • Authors

    • P. K. Iyengar Scientific Computing Researcher, BARC, India Author

    DOI:

    https://doi.org/10.67228/30716357/IJMRSE-2024PII5J9A

    Published 10-05-2024

  • Artificial Intelligence (Ai), Sustainable Materials Discovery, Machine Learning, Deep Learning, Materials Informatics, Computational Materials Science, Green Engineering, Materials Genome Initiative, High-Throughput Screening, Explainable Ai (Xai), Life Cycle Assessment (Lca), Digital Twins, Multi-Objective Optimization, Engineering Materials, Smart Manufacturing, Circular Economy, Industry 5.0, Predictive Modeling, Data-Driven Materials Design, Advanced Engineering Applications

    Issue

    Vol. 7 No. 2 (2024)

    Section

    Articles

    How to Cite

    AI-Driven Sustainable Materials Discovery for Next-Generation Engineering Applications. (2024). International Journal of Modern Research in Science & Engineering, 7(2), 01-19. https://doi.org/10.67228/30716357/IJMRSE-2024PII5J9A

  • Abstract

    The increasing demand for sustainable, high-performance materials has accelerated the adoption of Artificial Intelligence (AI) in materials discovery. Traditional material development relies on time-consuming experiments and computationally intensive simulations, limiting scalability and innovation. AI-driven materials discovery integrates machine learning, deep learning, data analytics, and computational materials science to rapidly predict, optimize, and design advanced materials with improved mechanical, thermal, electrical, and environmental performance. The proposed framework combines data preprocessing, feature engineering, predictive modeling, optimization, and sustainability assessment to identify materials that satisfy both engineering and environmental requirements. Advanced algorithms, including Random Forest, Support Vector Machines, Neural Networks, and Gradient Boosting, accurately predict material properties, while generative AI enables inverse design of novel, recyclable, and energy-efficient materials. Sustainability metrics such as life-cycle assessment and carbon footprint guide multi-objective optimization. Despite challenges in data quality and validation, emerging technologies including federated learning, physics-informed neural networks, digital twins, and autonomous laboratories are expected to further advance AI-enabled sustainable materials discovery for Industry 5.0 and resource-efficient engineering.

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