The spread of data-driven materials research has increased the need for systematically designed materials property databases.However,the development of polymer databases has lagged far behind other material systems.We...The spread of data-driven materials research has increased the need for systematically designed materials property databases.However,the development of polymer databases has lagged far behind other material systems.We present RadonPy,an open-source library that can automate the complete process of all-atom classical molecular dynamics(MD)simulations applicable to a wide variety of polymeric materials.Herein,15 different properties were calculated for more than 1000 amorphous polymers.The MD-calculated properties were systematically compared with experimental data to validate the calculation conditions;the bias and variance in the MD-calculated properties were successfully calibrated by a machine learning technique.During the high-throughput data production,we identified eight amorphous polymers with extremely high thermal conductivity(>0.4 W∙m^(–1)∙K^(–1))and their underlying mechanisms.Similar to the advancement of materials informatics since the advent of computational property databases for inorganic crystals,database construction using RadonPy will promote the development of polymer informatics.展开更多
基金supported by Innovation Program for Quantum Science and Technology (2021ZD0300200)Shanghai Municipal Science and Technology Major Project (2019SHZDZX01)+13 种基金Special funds from Jinan Science and Technology Bureau and Jinan High Tech Zone Management Committeethe Chinese Academy of Sciences (CAS)Anhui Initiative in Quantum Information TechnologiesTechnology Committee of Shanghai MunicipalityNatural Science Foundation of Shandong Province (ZR202209080019)Key-Area Research and Development Program of Guangdong Provice (2020B0303030001)supported in part by the Japanese MEXT Quantum Leap Flagship Program (MEXT Q-LEAP,JPMXS0118069605)the support from the Youth Talent Lifting Project (2020-JCJQ-QT-030)the National Natural Science Foundation of China (12274464,and 11905294)China Postdoctoral Science Foundationthe Open Research Fund from State Key Laboratory of High Performance Computing of China (201901-01)supported by Shanghai Rising-Star Program (23QA1410000)the Youth Innovation Promotion Association of CAS (2022460)the support from THE XPLORER PRIZE。
基金The numerical calculations were conducted on the five supercomputer systems,Fugaku at the RIKEN Center for Computational Science,Kobe,Japanthe supercomputer at the Research Center for Computational Science,Okazaki,Japan(Project:21-IMS-C126,22-IMS-C125)+7 种基金the supercomputer Ohtaka at the Supercomputer Center,the Institute for Solid State Physics,the University of Tokyo,Tokyo,Japanthe supercomputer TSUBAME3.0 at the Tokyo Institute of Technology,Tokyo,Japanthe supercomputer ABCI at the National Institute of Advanced Industrial Science and Technology,Tsukuba,JapanThis work was supported by the following five grants:a JST CREST(Grant Number JPMJCR19I3 to J.M.and R.Y.)the MEXT as“Program for Promoting Researches on the Supercomputer Fugaku”(Project ID:hp210264 to R.Y.)the Grant-in-Aid for Scientific Research(A)from the Japan Society for the Promotion of Science(19H01132 to R.Y.)the Grant-in-Aid for Scientific Research(C)from the Japan Society for the Promotion of Science(22K11949 to Y.H.)the HPCI System Research Project(Project ID:hp210213 to Y.H.).
文摘The spread of data-driven materials research has increased the need for systematically designed materials property databases.However,the development of polymer databases has lagged far behind other material systems.We present RadonPy,an open-source library that can automate the complete process of all-atom classical molecular dynamics(MD)simulations applicable to a wide variety of polymeric materials.Herein,15 different properties were calculated for more than 1000 amorphous polymers.The MD-calculated properties were systematically compared with experimental data to validate the calculation conditions;the bias and variance in the MD-calculated properties were successfully calibrated by a machine learning technique.During the high-throughput data production,we identified eight amorphous polymers with extremely high thermal conductivity(>0.4 W∙m^(–1)∙K^(–1))and their underlying mechanisms.Similar to the advancement of materials informatics since the advent of computational property databases for inorganic crystals,database construction using RadonPy will promote the development of polymer informatics.
