LOCic是针对欧洲核子中心LHC的ATLAS中液氩量能器前端电子单元的两通道、耐辐射、低功耗高速串行传输器。该系统工作在强微粒子辐射环境下,其高速数据采集和传输会出现突发的多位连续数据位错和数据流位滑。针对上述情况,基于Stratix II...LOCic是针对欧洲核子中心LHC的ATLAS中液氩量能器前端电子单元的两通道、耐辐射、低功耗高速串行传输器。该系统工作在强微粒子辐射环境下,其高速数据采集和传输会出现突发的多位连续数据位错和数据流位滑。针对上述情况,基于Stratix II GX FPGA设计了模拟以上差错现象的注入器,用于后端数据解码和恢复系统的设计与测试。测试和实验结果表明,该差错注入器有效可行。展开更多
Monte Carlo(MC)methods are important computational tools for molecular structure optimizations and predictions.When solvent effects are explicitly considered,MC methods become very expensive due to the large degree of...Monte Carlo(MC)methods are important computational tools for molecular structure optimizations and predictions.When solvent effects are explicitly considered,MC methods become very expensive due to the large degree of freedom associated with the water molecules and mobile ions.Alternatively implicit-solvent MC can largely reduce the computational cost by applying a mean field approximation to solvent effects and meanwhile maintains the atomic detail of the target molecule.The two most popular implicit-solvent models are the Poisson-Boltzmann(PB)model and the Generalized Born(GB)model in a way such that the GB model is an approximation to the PB model but is much faster in simulation time.In this work,we develop a machine learning-based implicit-solvent Monte Carlo(MLIMC)method by combining the advantages of both implicit solvent models in accuracy and efficiency.Specifically,the MLIMC method uses a fast and accurate PB-based machine learning(PBML)scheme to compute the electrostatic solvation free energy at each step.We validate our MLIMC method by using a benzene-water system and a protein-water system.We show that the proposed MLIMC method has great advantages in speed and accuracy for molecular structure optimization and prediction.展开更多
文摘LOCic是针对欧洲核子中心LHC的ATLAS中液氩量能器前端电子单元的两通道、耐辐射、低功耗高速串行传输器。该系统工作在强微粒子辐射环境下,其高速数据采集和传输会出现突发的多位连续数据位错和数据流位滑。针对上述情况,基于Stratix II GX FPGA设计了模拟以上差错现象的注入器,用于后端数据解码和恢复系统的设计与测试。测试和实验结果表明,该差错注入器有效可行。
基金supported in part by NIH grant GM126189NSF grants DMS-2052983,DMS-1761320+3 种基金IIS-1900473NASA grant 80NSSC21M0023Michigan Economic Development Corporation,MSU Foundation,Bristol-Myers Squibb 65109,and Pfizersupported in part by NSF grants DMS1819193 and DMS-2110922。
文摘Monte Carlo(MC)methods are important computational tools for molecular structure optimizations and predictions.When solvent effects are explicitly considered,MC methods become very expensive due to the large degree of freedom associated with the water molecules and mobile ions.Alternatively implicit-solvent MC can largely reduce the computational cost by applying a mean field approximation to solvent effects and meanwhile maintains the atomic detail of the target molecule.The two most popular implicit-solvent models are the Poisson-Boltzmann(PB)model and the Generalized Born(GB)model in a way such that the GB model is an approximation to the PB model but is much faster in simulation time.In this work,we develop a machine learning-based implicit-solvent Monte Carlo(MLIMC)method by combining the advantages of both implicit solvent models in accuracy and efficiency.Specifically,the MLIMC method uses a fast and accurate PB-based machine learning(PBML)scheme to compute the electrostatic solvation free energy at each step.We validate our MLIMC method by using a benzene-water system and a protein-water system.We show that the proposed MLIMC method has great advantages in speed and accuracy for molecular structure optimization and prediction.
