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
Machine learning has emerged as a novel tool for the efficient prediction of material properties,and claims have been made that machine-learned models for the formation energy of compounds can approach the accuracy of...Machine learning has emerged as a novel tool for the efficient prediction of material properties,and claims have been made that machine-learned models for the formation energy of compounds can approach the accuracy of Density Functional Theory(DFT).The models tested in this work include five recently published compositional models,a baseline model using stoichiometry alone,and a structural model.By testing seven machine learning models for formation energy on stability predictions using the Materials Project database of DFT calculations for 85,014 unique chemical compositions,we show that while formation energies can indeed be predicted well,all compositional models perform poorly on predicting the stability of compounds,making them considerably less useful than DFT for the discovery and design of new solids.Most critically,in sparse chemical spaces where few stoichiometries have stable compounds,only the structural model is capable of efficiently detecting which materials are stable.The nonincremental improvement of structural models compared with compositional models is noteworthy and encourages the use of structural models for materials discovery,with the constraint that for any new composition,the ground-state structure is not known a priori.This work demonstrates that accurate predictions of formation energy do not imply accurate predictions of stability,emphasizing the importance of assessing model performance on stability predictions,for which we provide a set of publicly available tests.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
Half-Heusler materials are strong candidates for thermoelectric applications due to their high weighted mobilities and power factors,which is known to be correlated to valley degeneracy in the electronic band structur...Half-Heusler materials are strong candidates for thermoelectric applications due to their high weighted mobilities and power factors,which is known to be correlated to valley degeneracy in the electronic band structure.However,there are over 50 known semiconducting half-Heusler phases,and it is not clear how the chemical composition affects the electronic structure.While all the n-type electronic structures have their conduction band minimum at either theΓ-or X-point,there is more diversity in the p-type electronic structures,and the valence band maximum can be at either theΓ-,L-,or W-point.Here,we use high throughput computation and machine learning to compare the valence bands of known half-Heusler compounds and discover new chemical guidelines for promoting the highly degenerate W-point to the valence band maximum.We do this by constructing an“orbital phase diagram”to cluster the variety of electronic structures expressed by these phases into groups,based on the atomic orbitals that contribute most to their valence bands.Then,with the aid of machine learning,we develop new chemical rules that predict the location of the valence band maximum in each of the phases.These rules can be used to engineer band structures with band convergence and high valley degeneracy.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 primarily funded by the U.S.Department of Energy,Office of Science,Office of Basic Energy Sciences,Materials Sciences and Engineering Division under Contract No.DE-AC02-05-CH11231(Materials Project program KC23MP)This research also used the Savio computational cluster resource provided by the Berkeley Research Computing program at the University of California,Berkeley(supported by the UC Berkeley Chancellor,Vice Chancellor for Research,and Chief Information Officer)and 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).
文摘Machine learning has emerged as a novel tool for the efficient prediction of material properties,and claims have been made that machine-learned models for the formation energy of compounds can approach the accuracy of Density Functional Theory(DFT).The models tested in this work include five recently published compositional models,a baseline model using stoichiometry alone,and a structural model.By testing seven machine learning models for formation energy on stability predictions using the Materials Project database of DFT calculations for 85,014 unique chemical compositions,we show that while formation energies can indeed be predicted well,all compositional models perform poorly on predicting the stability of compounds,making them considerably less useful than DFT for the discovery and design of new solids.Most critically,in sparse chemical spaces where few stoichiometries have stable compounds,only the structural model is capable of efficiently detecting which materials are stable.The nonincremental improvement of structural models compared with compositional models is noteworthy and encourages the use of structural models for materials discovery,with the constraint that for any new composition,the ground-state structure is not known a priori.This work demonstrates that accurate predictions of formation energy do not imply accurate predictions of stability,emphasizing the importance of assessing model performance on stability predictions,for which we provide a set of publicly available tests.
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.
基金M.T.D.and G.J.S.acknowledge support from the National Science Foundation(DMREF-1333335 and DMREF-1729487)S.A.and G.J.S.acknowledge the U.S.Department of Energy,Office of Energy Efficiency and Renewable Energy(EERE)program“Accelerated Discovery of Compositionally Complex Alloys for Direct Thermal Energy Conversion”(DOE award DE-AC02-76SF00515)+1 种基金A.D and A.J.were supported by the United States Department of Energy,Office of Basic Energy Sciences,Early Career Research Program under award DE-AC02-05CH11231which funds Lawrence Berkeley National Laboratory.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Half-Heusler materials are strong candidates for thermoelectric applications due to their high weighted mobilities and power factors,which is known to be correlated to valley degeneracy in the electronic band structure.However,there are over 50 known semiconducting half-Heusler phases,and it is not clear how the chemical composition affects the electronic structure.While all the n-type electronic structures have their conduction band minimum at either theΓ-or X-point,there is more diversity in the p-type electronic structures,and the valence band maximum can be at either theΓ-,L-,or W-point.Here,we use high throughput computation and machine learning to compare the valence bands of known half-Heusler compounds and discover new chemical guidelines for promoting the highly degenerate W-point to the valence band maximum.We do this by constructing an“orbital phase diagram”to cluster the variety of electronic structures expressed by these phases into groups,based on the atomic orbitals that contribute most to their valence bands.Then,with the aid of machine learning,we develop new chemical rules that predict the location of the valence band maximum in each of the phases.These rules can be used to engineer band structures with band convergence and high valley degeneracy.
