Recently developed physics-informed neural network (PINN) has achieved success in many science and engineering disciplines by encoding physics laws into the loss functions of network, such that not only conforms to measurements, initial boundary conditions but also satisfies governing equations. This work first investigates performance PINN solving stiff chemical kinetic problems with equations...