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

Artificial-intelligence modeling of chemical reactions is advancing rapidly but typically depends on large, costly experimental datasets. We introduce SymChemAI, a chemically informed neural network that simulates reaction dynamics directly from symbolic equations. SymChemAI automatically maps balanced reactions to a system of ordinary differential equations derived from fundamental physicochemical laws. Its objective functions explicitly enforce kinetic rate laws, thermodynamic constraints, mass conservation, non-negativity of concentrations, and compliance with initial conditions. This physics-anchored design contrasts with conventional PINN or Transformer approaches by prioritizing hard scientific constraints and improving chemical interpretability. Without requiring experimental supervision, SymChemAI predicts conversions, yields, and full concentration–time profiles with consistent physical behavior. The framework unifies symbolic reasoning and deep learning, enabling virtual screening, process optimization, and robust reaction design in silico. A forthcoming digital workflow further supports transparency and reproducibility. This work is available as a preprint on ChemRxiv [1], accelerating community feedback and benchmarking. Overall, SymChemAI offers a modular, data-light route to credible reaction simulation that adheres to first principles while retaining the flexibility and expressivity of modern neural networks.