AI-Enabled Molecular Simulation: Advancing Computational Approaches to Chemical and Material Discovery

Linda Lee

AI-Enabled Molecular Simulation: Advancing Computational Approaches to Chemical and Material Discovery

Author(s)	Linda Lee
Country	United States
Abstract	Artificial intelligence is rapidly transforming molecular simulation by enabling faster prediction, surrogate modelling, molecular generation and intelligent exploration of chemical and materials design spaces. Conventional molecular simulation methods, including molecular dynamics, density functional theory and quantum-chemical calculations, provide powerful descriptions of molecular and material behaviour but can become computationally expensive when applied to large systems, long timescales or extensive design spaces. AI-enabled molecular simulation offers an emerging computational paradigm in which machine learning models learn molecular representations, approximate expensive calculations, accelerate simulation workflows and guide the search for novel chemical structures and materials. This paper examines the integration of artificial intelligence with molecular simulation for chemical and materials discovery. It reviews the roles of graph neural networks, equivariant neural networks, neural interatomic potentials, generative models, reinforcement learning and active learning in accelerating molecular prediction and simulation. A conceptual framework is proposed that integrates AI-based property prediction, physics-based simulation, uncertainty estimation, active learning and experimental validation. Applications in drug discovery, catalyst design, battery materials, polymers, photovoltaics, carbon-capture materials and sustainable chemistry are examined. Particular attention is given to the importance of physical consistency, data quality, uncertainty quantification, interpretability and validation. The analysis suggests that AI should not simply replace established simulation techniques; rather, the strongest approach is a hybrid architecture in which machine learning accelerates computationally expensive calculations while physics-based models provide constraints and scientific grounding. Such systems could significantly expand the accessible chemical and materials design space and shorten the cycle from molecular hypothesis to validated discovery.
Keywords	AI-Enabled Molecular Simulation, Molecular Dynamics, Machine Learning, Computational Chemistry, Materials Discovery, Graph Neural Networks, Neural Interatomic Potentials, Generative AI, Drug Discovery, Computational Materials Science.
Field	Engineering
Published In	Volume 7, Issue 4, July-August 2025
Published On	2025-07-22

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American Journal of Advanced Multidisciplinary Research and Innovation

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AI-Enabled Molecular Simulation: Advancing Computational Approaches to Chemical and Material Discovery

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