THE INTEGRATION OF MACHINE LEARNING AND PREDICTIVE MODELING: A NEW ERA IN COMPUTATIONAL CHEMISTRY AND MATERIALS SCIENCE

This review provides an in-depth analysis of the transformative integration of machine learning (ML) into computational chemistry and materials science, emphasizing its potential to revolutionize predictive modeling and accelerate discovery. Traditional computational techniques, such as density functional theory (DFT) and molecular dynamics, although effective, are constrained by high computational costs and scalability limitations. ML, with its ability to process complex, high-dimensional datasets, addresses these challenges by enabling rapid and accurate predictions of molecular properties, reaction mechanisms, and material behaviors. These advancements are driving progress in critical applications, such as drug discovery, where ML accelerates virtual screening and binding affinity predictions, and materials design, which benefits from faster identification of novel materials with tailored properties. The review also delves into the challenges impeding broader ML adoption, including data scarcity, bias in training datasets, over fitting, and the interpretability of complex ML models. Strategies to overcome these barriers, such as feature engineering, explainable AI, and the development of comprehensive, high-quality datasets, are explored. Furthermore, the importance of interdisciplinary collaboration among chemists, material scientists, and computer scientists is underscored, as such partnerships are vital for advancing ML-driven approaches. Future directions are discussed, including the integration of ML with multi-scale modeling, leveraging quantum computing for enhanced simulations, and improving explain ability to foster trust and adoption. This work highlights ML’s potential to drive groundbreaking innovations in energy, healthcare, and sustainable materials, establishing it as a cornerstone for the future of computational science.