The original version of the Article contained an error in Fig.3,in which the label at the top of the first column of Fig.3 originally incorrectly read‘Yield Strength(GPa)’,rather than the correct‘Yield Strength(MPa...The original version of the Article contained an error in Fig.3,in which the label at the top of the first column of Fig.3 originally incorrectly read‘Yield Strength(GPa)’,rather than the correct‘Yield Strength(MPa)’.This has been corrected in both the PDF and HTML versions of the Article.展开更多
We present a benchmark test suite and an automated machine learning procedure for evaluating supervised machine learning(ML)models for predicting properties of inorganic bulk materials.The test suite,Matbench,is a set...We present a benchmark test suite and an automated machine learning procedure for evaluating supervised machine learning(ML)models for predicting properties of inorganic bulk materials.The test suite,Matbench,is a set of 13 ML tasks that range in size from 312 to 132k samples and contain data from 10 density functional theory-derived and experimental sources.展开更多
文摘The original version of the Article contained an error in Fig.3,in which the label at the top of the first column of Fig.3 originally incorrectly read‘Yield Strength(GPa)’,rather than the correct‘Yield Strength(MPa)’.This has been corrected in both the PDF and HTML versions of the Article.
基金This work was intellectually led and funded by the United States Department of Energy,Office of Basic Energy Sciences,Early Career Research Program,which provided funding for A.D.,Q.W.,A.G.,D.D.,and A.J.Lawrence Berkeley National Laboratory is funded by the DOE under award DE-AC02-05CH11231This research used the Lawrencium computational cluster resource provided by the IT Division at the Lawrence Berkeley National Laboratory(Supported by the Director,Office of Science,Office of Basic Energy Sciences,of the U.S.Department of Energy under Contract No.DE-AC02-05CH11231)This research used resources of the National Energy Research Scientific Computing Center(NERSC),a U.S.Department of Energy Office of Science User Facility operated under Contract No.DE-AC02-05CH11231.
文摘We present a benchmark test suite and an automated machine learning procedure for evaluating supervised machine learning(ML)models for predicting properties of inorganic bulk materials.The test suite,Matbench,is a set of 13 ML tasks that range in size from 312 to 132k samples and contain data from 10 density functional theory-derived and experimental sources.
