The computational prediction of superhard materials would enable the in silico design of compounds that could be used in a wide variety of technological applications.Herein,good agreement was found between experimenta...The computational prediction of superhard materials would enable the in silico design of compounds that could be used in a wide variety of technological applications.Herein,good agreement was found between experimental Vickers hardnesses,Hv,of a wide range of materials and those calculated by three macroscopic hardness models that employ the shear and/or bulk moduli obtained from:(i)first principles via AFLOW-AEL(AFLOW Automatic Elastic Library),and(ii)a machine learning(ML)model trained on materials within the AFLOW repository.Because H^(ML)_(v) values can be quickly estimated,they can be used in conjunction with an evolutionary search to predict stable,superhard materials.This methodology is implemented in the XTALOPT evolutionary algorithm.Each crystal is minimized to the nearest local minimum,and its Vickers hardness is computed via a linear relationship with the shear modulus discovered by Teter.Both the energy/enthalpy and H^(ML)_(v),Teter are employed to determine a structure’s fitness.This implementation is applied towards the carbon system,and 43 new superhard phases are found.A topological analysis reveals that phases estimated to be slightly harder than diamond contain a substantial fraction of diamond and/or lonsdaleite.展开更多
基金We acknowledge the DOD-ONR(N00014-16-1-2583 and N00014-17-1-2090)for financial support,and the Center for Computational Research(CCR)at SUNY Buffalo for computational supportD.M.P.thanks the Universitádegi Studi di Milano for the transition grants PSR2015-1718 and FFABR2018.
文摘The computational prediction of superhard materials would enable the in silico design of compounds that could be used in a wide variety of technological applications.Herein,good agreement was found between experimental Vickers hardnesses,Hv,of a wide range of materials and those calculated by three macroscopic hardness models that employ the shear and/or bulk moduli obtained from:(i)first principles via AFLOW-AEL(AFLOW Automatic Elastic Library),and(ii)a machine learning(ML)model trained on materials within the AFLOW repository.Because H^(ML)_(v) values can be quickly estimated,they can be used in conjunction with an evolutionary search to predict stable,superhard materials.This methodology is implemented in the XTALOPT evolutionary algorithm.Each crystal is minimized to the nearest local minimum,and its Vickers hardness is computed via a linear relationship with the shear modulus discovered by Teter.Both the energy/enthalpy and H^(ML)_(v),Teter are employed to determine a structure’s fitness.This implementation is applied towards the carbon system,and 43 new superhard phases are found.A topological analysis reveals that phases estimated to be slightly harder than diamond contain a substantial fraction of diamond and/or lonsdaleite.
