The Role of Artificial Intelligence in Formulating and Balancing Chemical Equations for Organic Compounds

Abstract

Balancing chemical equations for organic compounds is a routine yet often tedious component of chemistry education and computational chemistry, and it becomes markedly harder as molecular size and functional-group complexity grow. This paper reviews how artificial intelligence (AI) techniques, ranging from classical algebraic and matrix-based solvers to modern machine-learning models such as graph neural networks and sequence-to-sequence transformers, are being used to formulate, balance, and validate chemical equations for organic reactions. We synthesise findings from the computational chemistry, cheminformatics, and chemistry-education literature to compare rule-based, algebraic, and learned approaches, and we illustrate the underlying logic with a worked example of ethanol combustion solved through the matrix null-space method. The review indicates that deterministic algebraic methods remain the most reliable choice for routine stoichiometric balancing, while learned models add clear value for predicting reaction outcomes and retrosynthetic routes in more complex organic transformations, albeit with lower top-1 accuracy and weaker interpretability. We further discuss the growing use of generative AI tools in chemistry classrooms, including in low-resource settings such as Afghan universities, and conclude that a hybrid architecture combining deterministic solvers with learned chemical-plausibility models is the most promising direction for AI-assisted equation balancing