Accurate and efficient molecular dynamics based on machine learning and non von Neumann architecture

Force field-based classical molecular dynamics(CMD)is efficient but its potential energy surface(PES)prediction error can be very large.Density functional theory(DFT)-based ab-initio molecular dynamics(AIMD)is accurate but computational cost limits its applications to small systems.Here,we propose a molecular dynamics(MD)methodology which can simultaneously achieve both AIMD-level high accuracy and CMD-level high efficiency.The high accuracy is achieved by exploiting deep neural network(DNN)’s arbitrarily-high precision to fit PES.The high efficiency is achieved by deploying multiplication-less DNN on a carefully-optimized special-purpose non von Neumann(NvN)computer to mitigate the performance-limiting data shuttling(i.e.,‘memory wall bottleneck’).By testing on different molecules and bulk systems,we show that the proposed MD methodology is generally-applicable to various MD tasks.The proposed MD methodology has been deployed on an in-house computing server based on reconfigurable field programmable gate array(FPGA),which is freely available at http://nvnmd.picp.vip.