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 accurat...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.展开更多
基金This work is supported by the National Natural Science Foundation of China(#61804049)the Fundamental Research Funds for the Central Universities of P.R.China+3 种基金Huxiang High Level Talent Gathering Project(#2019RS1023)the Key Research and Development Project of Hunan Province,P.R.China(#2019GK2071)the Technology Innovation and Entrepreneurship Funds of Hunan Province,P.R.China(#2019GK5029)the Fund for Distinguished Young Scholars of Changsha(#kq1905012).
文摘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.
