Searching and designing new materials play crucial roles in the development of energy storage devices. In today's world where machine learning technology has shown strong predictive ability for various tasks, the ...Searching and designing new materials play crucial roles in the development of energy storage devices. In today's world where machine learning technology has shown strong predictive ability for various tasks, the combination with machine learning technology will accelerate the process of material development. Herein, we develop ESM Cloud Toolkit for energy storage materials based on Mat Elab platform, which is designed as a convenient and accurate way to automatically record and save the raw data of scientific research. The ESM Cloud Toolkit includes multiple features such as automatic archiving of computational simulation data, post-processing of experimental data, and machine learning applications. It makes the entire research workflow more automated and reduces the entry barrier for the application of machine learning technology in the domain of energy storage materials. It integrates data archive, traceability, processing, and reutilization, and allows individual research data to play a greater role in the era of AI.展开更多
This study assesses the ability of 10 Earth System Models(ESMs)that participated in the Coupled Model Intercomparison Project Phase 6(CMIP6)to reproduce the present-day inhalable particles with diameters less than 2.5...This study assesses the ability of 10 Earth System Models(ESMs)that participated in the Coupled Model Intercomparison Project Phase 6(CMIP6)to reproduce the present-day inhalable particles with diameters less than 2.5 micrometers(PM_(2.5))over Asia and discusses the uncertainty.PM_(2.5)accounts for more than 30%of the surface total aerosol(fine and coarse)concentration over Asia,except for central Asia.The simulated spatial distributions of PM_(2.5)and its components,averaged from 2005 to 2020,are consistent with the Modern-Era Retrospective Analysis for Research and Applications version 2(MERRA-2)reanalysis.They are characterized by the high PM_(2.5)concentrations in eastern China and northern India where anthropogenic components such as sulfate and organic aerosol dominate,and in northwestern China where the mineral dust in PM_(2.5)fine particles(PM_(2.5)DU)dominates.The present-day multimodel mean(MME)PM_(2.5)concentrations slightly underestimate ground-based observations in the same period of 2014–2019,although observations are affected by the limited coverage of observation sites and the urban areas.Those model biases partly come from other aerosols(such as nitrate and ammonium)not involved in our analyses,and also are contributed by large uncertainty in PM_(2.5)simulations on local scale among ESMs.The model uncertainties over East Asia are mainly attributed to sulfate and PM_(2.5)DU;over South Asia,they are attributed to sulfate,organic aerosol,and PM_(2.5)DU;over Southeast Asia,they are attributed to sea salt in PM_(2.5)fine particles(PM_(2.5)SS);and over central Asia,they are attributed to PM_(2.5)DU.They are mainly caused by the different representations of aerosols within individual ESMs including the representation of aerosol size distributions,dynamic transport,and physical and chemistry mechanisms.展开更多
基金supported by the National Natural Science Foundation of China (Grant Nos. 52022106 and 52172258)the Informatization Plan of Chinese Academy of Sciences (Grant No. CASWX2021SF-0102)。
文摘Searching and designing new materials play crucial roles in the development of energy storage devices. In today's world where machine learning technology has shown strong predictive ability for various tasks, the combination with machine learning technology will accelerate the process of material development. Herein, we develop ESM Cloud Toolkit for energy storage materials based on Mat Elab platform, which is designed as a convenient and accurate way to automatically record and save the raw data of scientific research. The ESM Cloud Toolkit includes multiple features such as automatic archiving of computational simulation data, post-processing of experimental data, and machine learning applications. It makes the entire research workflow more automated and reduces the entry barrier for the application of machine learning technology in the domain of energy storage materials. It integrates data archive, traceability, processing, and reutilization, and allows individual research data to play a greater role in the era of AI.
基金Supported by the National Key Research and Development Program of China(2016YFA0602100)UK–China Research&Innovation Partnership Fund through the Met Office Climate Science for Service Partnership(CSSP)China as part of the Newton Fund.
文摘This study assesses the ability of 10 Earth System Models(ESMs)that participated in the Coupled Model Intercomparison Project Phase 6(CMIP6)to reproduce the present-day inhalable particles with diameters less than 2.5 micrometers(PM_(2.5))over Asia and discusses the uncertainty.PM_(2.5)accounts for more than 30%of the surface total aerosol(fine and coarse)concentration over Asia,except for central Asia.The simulated spatial distributions of PM_(2.5)and its components,averaged from 2005 to 2020,are consistent with the Modern-Era Retrospective Analysis for Research and Applications version 2(MERRA-2)reanalysis.They are characterized by the high PM_(2.5)concentrations in eastern China and northern India where anthropogenic components such as sulfate and organic aerosol dominate,and in northwestern China where the mineral dust in PM_(2.5)fine particles(PM_(2.5)DU)dominates.The present-day multimodel mean(MME)PM_(2.5)concentrations slightly underestimate ground-based observations in the same period of 2014–2019,although observations are affected by the limited coverage of observation sites and the urban areas.Those model biases partly come from other aerosols(such as nitrate and ammonium)not involved in our analyses,and also are contributed by large uncertainty in PM_(2.5)simulations on local scale among ESMs.The model uncertainties over East Asia are mainly attributed to sulfate and PM_(2.5)DU;over South Asia,they are attributed to sulfate,organic aerosol,and PM_(2.5)DU;over Southeast Asia,they are attributed to sea salt in PM_(2.5)fine particles(PM_(2.5)SS);and over central Asia,they are attributed to PM_(2.5)DU.They are mainly caused by the different representations of aerosols within individual ESMs including the representation of aerosol size distributions,dynamic transport,and physical and chemistry mechanisms.
