分布式信息能源系统(distributed cyber energy system,DCES)是能源互联网(energy Internet,EI)顺应新时代、新机遇与新挑战的产物,是能源发展的新阶段与新形态。能源作为信息的载体催生出海量能源数据,而信息技术的发展又将反哺能源网...分布式信息能源系统(distributed cyber energy system,DCES)是能源互联网(energy Internet,EI)顺应新时代、新机遇与新挑战的产物,是能源发展的新阶段与新形态。能源作为信息的载体催生出海量能源数据,而信息技术的发展又将反哺能源网络的改造和升级。DCES通过信息流与能量流的交互影响,加强能源互联网的计算、优化、控制与管理的深度融合,以安全、高效、可靠和实时的方式感知与管控能源系统,实现多能源系统之间的自治与协作。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
文摘分布式信息能源系统(distributed cyber energy system,DCES)是能源互联网(energy Internet,EI)顺应新时代、新机遇与新挑战的产物,是能源发展的新阶段与新形态。能源作为信息的载体催生出海量能源数据,而信息技术的发展又将反哺能源网络的改造和升级。DCES通过信息流与能量流的交互影响,加强能源互联网的计算、优化、控制与管理的深度融合,以安全、高效、可靠和实时的方式感知与管控能源系统,实现多能源系统之间的自治与协作。
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.
基金financial support from CNPq,CAPES,FACEPE,and FINEPagenciesfunded by the Public Call n.03 Produtividade em Pesquisa PROPESQ/PRPG/UF-PB project number PVN13305-2020,and PROPESQ/CNPq/UFPB PIN11132-2019+3 种基金developed within the scope of the project CICECO-Aveiro Institute of Materials,UIDB/50011/2020 and UIDP/50011/2020financed by Portuguese funds through the FCT/MECco-financed by FEDER under the PT2020 Partnership Agreementpartial financial support under grants:Pronex APQ-0675-1.06/14,INCT-NANO-MARCS APQ-0549-1.06/17,APQ-1007-1.06/15,and CNPq-PQ fellowship(Proc.309177/2018-9)
