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 materi-als.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 mecha-nism exploration.Here we introduce our recent up-date 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 S7 and S8 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 cat-alytic 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 po-tential as the input.The obtained double-network potential CuCHO is robust in simulation and the introduction of S7 and S8 PTSDs can reduce the root-mean-square errors of energy by a factor of two.