High entropy materials (HEMs, e.g. high entropy alloys, high entropy ceramics) have gained increasing interests due to the possibility that they can provide challenge properties unattainable by traditional materials. Though a large number of HEMs have emerged, there is still in lack of theoretical predictions and simulations on HEMs, which is probably caused by the chemical complexity of HEMs. In this work, we demonstrate that the machine learning potentials developed in recent years can overcome the complexity of HEMs, and serve as powerful theoretical tools to simulate HEMs. A deep learning potential (DLP) for high entropy (Zr0.2Hf0.2Ti0.2Nb0.2Ta0.2)C is fitted with the prediction error in energy and force being 9.4 meV/atom and 217 meV/Å, respectively. The reliability and generality of the DLP are affirmed, since it can accurately predict lattice parameters and elastic constants of mono-phase carbides TMC (TM = Ti, Zr, Hf, Nb and Ta). Lattice constants (increase from 4.5707 Å to 4.6727 Å), thermal expansion coefficients (increase from 7.85×10-6 K-1 to 10.58×10-6 K-1), phonon thermal conductivities (decrease from 2.02 W·m-1·K-1 to 0.95 W·m-1·K-1), and elastic properties of high entropy (Zr0.2Hf0.2Ti0.2Nb0.2Ta0.2)C in temperature ranging from 0 °C to 2400 °C are predicted by molecular dynamics simulations. The predicted room temperature properties agree well with experimental measurements, indicating the high accuracy of the DLP. With introducing of machine learning potentials, many problems that are intractable by traditional methods can be handled now. It is hopeful that deep insight into HEMs can be obtained in the future by such powerful methods.