Non-Markovian intelligent dissipative particle dynamics integrated with machine learning for enhancing coarse-grained simulations

We propose a machine learning-based coarse-grained method that integrates molecular dynamics (MD) with dissipative particle dynamics (DPD) to address the limitations of the Markovian approximation in systems where particle motion and fluctuating forces exhibit overlapping time scales. Our approach, termed non-Markovian intelligent dissipative particle dynamics (NM-IDPD), utilizes MD data to train a neural network capable of predicting both conservative and dissipative forces within the DPD framework, effectively accounting for non-Markovian effects. We have also incorporated a pressure constraint mechanism into the neural network to accurately capture the system pressure, which is a challenging issue for most traditional coarse-grained methods. Through applications to star polymers, methane, and water systems, NM-IDPD has demonstrated good performance in replicating both the static and dynamic properties of simulated systems across various time scales. This advancement offers a promising avenue for material dynamics simulation, enhancing the accuracy and efficiency of computational modeling in complex systems.