摘要
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.
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-AC02-05CH11231
This 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.
