SymChemAI: A Symbolically Informed Neural Network for Physicochemical Modeling of Reaction Systems

The modeling of chemical reactions using artificial intelligence is rapidly advancing but still heavily relies on abundant and costly experimental data. In the context of computational chemistry, we present SymChemAI, a chemically informed neural network capable of simulating the dynamic evolution of reactions from their symbolic representation alone. This approach is based on the automatic translation of chemical equations into a differential system governed by fundamental physicochemical laws. The core of SymChemAI lies in its loss functions, designed to enforce the principles of kinetics (via ordinary differential equations), thermodynamic laws, mass conservation, positivity of concentrations, and adherence to initial conditions. Unlike conventional PINN- or Transformer-based approaches, this formulation ensures the physical consistency of the model while enhancing its chemical interpretability. SymChemAI combines symbolic precision with the predictive power of deep learning. It reliably predicts conversions, yields, and reaction profiles, while eliminating the need for experimental supervision. Thus, this AI based computational chemistry paradigm provides symbolic and explainable method for intelligent simulation, process optimization, and advanced reaction design in virtual environments.