American Journal of Advanced Multidisciplinary Research and Innovation

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AI-Enabled Computational Discovery: Accelerating Innovation in Materials, Medicine and Environmental Science

Author(s) Imran Sayeed
Country United States
Abstract Artificial intelligence (AI) is increasingly transforming computational discovery by enabling researchers to analyse complex datasets, identify hidden patterns, predict system behaviour, and accelerate the development of novel materials, medicines, and environmental technologies. Conventional discovery processes often require extensive experimentation, large financial investments, and lengthy development cycles. AI-enabled computational approaches can reduce these constraints by combining machine learning, deep learning, generative models, high-throughput simulations, scientific knowledge graphs, and automated experimentation. This paper examines the emerging role of AI-enabled computational discovery across three major domains: materials science, medicine, and environmental science. A conceptual qualitative methodology is employed to analyse technological approaches, applications, benefits, challenges, and future research directions. In materials science, AI can support the prediction of material properties, discovery of catalysts, battery materials, semiconductors, and sustainable alternatives. In medicine, computational AI can accelerate drug discovery, molecular design, protein-structure analysis, biomarker identification, and personalised therapeutic development. In environmental science, AI can support pollutant prediction, climate modelling, ecosystem monitoring, environmental risk assessment, and the discovery of sustainable technologies. The paper proposes an integrated AI-enabled discovery framework based on data generation, representation learning, prediction, candidate generation, simulation, experimental validation, and continuous learning. The analysis indicates that AI is most effective when combined with domain knowledge, physics-based modelling, laboratory experimentation, and rigorous validation. Major challenges include data quality, interpretability, model uncertainty, computational cost, reproducibility, bias, intellectual property, and the gap between computational predictions and real-world performance. The paper concludes that AI-enabled computational discovery can substantially accelerate scientific innovation, but its long-term value will depend on trustworthy models, high-quality datasets, interdisciplinary collaboration, and integration with automated experimentation.
Keywords Artificial Intelligence, Computational Discovery, Machine Learning, Materials Science, Drug Discovery, Environmental Science, Generative AI, Scientific Computing, Molecular Design, Sustainable Innovation.
Field Engineering
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-04-30
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