Abstract: LASP (large-scale atomistic simulation with neural network potential) software developed by our group since 2018 is a powerful platform (www.lasphub.com) for performing atomic simulation of complex materials. The software integrates the neural network (NN) potential technique with the global potential energy surface exploration method, and thus can be utilized widely for structure prediction and reaction mechanism exploration. Here we introduce our recent update on the LASP program version 3.0, focusing on the new functionalities including the advanced neural network training based on the multi-network framework, the newly-introduced S^7 and S^8 power type structure descriptor (PTSD). These new functionalities are designed to further improve the accuracy of potentials and accelerate the neural network training for multiple-element systems. Taking Cu - C - H - O neural network potential and a heterogeneous catalytic model as the example, we show that these new functionalities can accelerate the training of multi-element neural network potential by using the existing single-network potential as the input. The obtained double-network potential CuCHO is robust in simulation and the introduction of S^7 and S^8 PTSDs can reduce the root-mean-square errors of energy by a factor of two.