MatterSim is a deep learning atomistic model designed for computational materials science. It uses graph neural network architectures to predict material properties and behavior at the atomic scale. The model is trained on quantum mechanical simulation data to learn interatomic potentials and material properties across different chemical elements, temperature ranges, and pressure conditions. It enables researchers to perform large-scale atomistic simulations without relying exclusively on expensive quantum chemistry calculations.