Artificial Intelligence in Computational and Materials Chemistry: Prospects and Limitations
David B. Olawadea b c✉, Oluwaseun Fapohundad, Sunday Oluwadamilola Usmane, Abiola D. Akintayof, Ayokunle Olalekan Igeg, Yemi Adekola Adekunleh i, Adedapo Oluwasanu Adeolaj✉
- aDepartment of Allied and Public Health, School of Health, Sport and Bioscience, University of East London, London, UK
- bDepartment of Public Health, York St John University, London, UK
- cDepartment of Research and Innovation, Medway NHS Foundation Trust, Gillingham, ME7 5NY, UK
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Abstract
Abstract Computational chemistry, at the intersection of theoretical chemistry and computer science, employs various models to analyze molecular structures and properties, enabling the understanding and prediction of intricate chemical processes. The integration of artificial intelligence (AI) has revolutionized several fields, particularly in materials chemistry, with applications spanning drug discovery, materials design, and quantum mechanics. However, challenges related to quantum system complexity, model interpretability, and data quality remain a few of the Achilles’ heel of AI applications. This paper provides an overview of AI’s evolution in computational and materials chemistry, focusing on several applications. AI’s transformative potential in materials chemistry is emphasized, facilitating precise material property predictions, crucial for industries reliant on materials innovation. In materials chemistry, AI has led to substantial advancements, enabling the rapid discovery of materials with tailored properties. Yet, the challenges of modeling complex quantum systems, achieving model interpretability, and accessing high-quality data remain. The integration of AI into computational and materials chemistry promises to reshape the field, revolutionizing chemical research, materials design, and technological innovation. In order to harness AI’s full potential, transparent AI models, advanced quantum simulations, optimized data utilization, scalable computing, interdisciplinary collaboration, and ethical AI practices are essential.