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@article{195649,
        author = {Aaditi D. Deshmukh and P. R. Mahalle},
        title = {Integration of Computational Chemistry and Artificial Intelligence for Advanced Material Design},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {no},
        pages = {13-21},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195649},
        abstract = {The integration of computational chemistry with artificial intelligence (AI) has emerged as a powerful strategy for accelerating advanced material design. In this study, a systematic framework combining first-principles simulations and machine learning techniques is proposed to predict key material properties efficiently and accurately. Density Functional Theory and molecular dynamics simulations were employed to generate reliable electronic, structural, and thermodynamic descriptors. These physically meaningful descriptors were subsequently used as input features for AI-based predictive models, including Random Forest Regression, Support Vector Regression, and Neural Networks. The results demonstrate that the selected descriptors exhibit strong correlations with target material properties, validating their suitability for data-driven modeling. Among the evaluated models, Neural Networks achieved the highest predictive accuracy, owing to their ability to capture complex, high-dimensional descriptor interactions. The close agreement between predicted and computationally calculated values confirms the robustness and reliability of the proposed workflow. Overall, this integrated approach significantly reduces computational cost while maintaining high prediction accuracy, offering an efficient pathway for large-scale material screening. The study highlights the potential of AI-assisted computational chemistry as a scalable and reliable tool for next-generation material discovery and optimization.},
        keywords = {Computational chemistry, Artificial intelligence, Machine learning, Density Functional Theory, Advanced material design.},
        month = {March},
        }