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@article{209714,
        author = {Dr.N.MD.Akram},
        title = {Artificial Intelligence in Chemistry: Applications, Challenges, and Pathways Toward Reliable and Sustainable Chemical Discovery},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {5},
        pages = {3097-3101},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=209714},
        abstract = {Artificial intelligence (AI) is transforming chemistry by enabling data-driven molecular design, reaction prediction, catalyst discovery, and materials optimization. Machine learning, deep learning, and generative models can accelerate chemical research by identifying patterns in complex datasets and reducing unnecessary experimental trials. However, the reliability of AI-driven chemical discovery remains limited by inconsistent datasets, model interpretability, prediction uncertainty, experimental reproducibility, and computational energy requirements. This review critically examines the applications of AI across chemical synthesis, computational chemistry, materials science, environmental chemistry, and sustainable manufacturing. Particular emphasis is placed on the integration of chemical knowledge, explainable AI, uncertainty quantification, experimental validation, and environmental assessment. By connecting predictive performance with chemical reliability and sustainability, this review identifies important research gaps and proposes a framework for more transparent, reproducible, and environmentally responsible AI-assisted chemical research.},
        keywords = {Artificial intelligence; machine learning; computational chemistry; molecular design; reaction prediction; catalysis; explainable AI; sustainable chemistry.},
        month = {October},
        }