Chemical design of SiO_(2)-based glasses with high elastic moduli and low weight is of great interest.However,it is difficult to find a universal expression to predict the elastic moduli according to the glass composi...Chemical design of SiO_(2)-based glasses with high elastic moduli and low weight is of great interest.However,it is difficult to find a universal expression to predict the elastic moduli according to the glass composition before synthesis since the elastic moduli are a complex function of interatomic bonds and their ordering at different length scales.Here we show that the densities and elastic moduli of SiO_(2)-based glasses can be efficiently predicted by machine learning(ML)techniques across a complex compositional space with multiple(>10)types of additive oxides besides SiO_(2).Our machine learning approach relies on a training set generated by high-throughput molecular dynamic(MD)simulations,a set of elaborately constructed descriptors that bridges the empirical statistical modeling with the fundamental physics of interatomic bonding,and a statistical learning/predicting model developed by implementing least absolute shrinkage and selection operator with a gradient boost machine(GBM-LASSO).展开更多
One of the most accurate approaches for calculating lattice thermal conductivity,κ_(l),is solving the Boltzmann transport equation starting from third-order anharmonic force constants.In addition to the underlying ap...One of the most accurate approaches for calculating lattice thermal conductivity,κ_(l),is solving the Boltzmann transport equation starting from third-order anharmonic force constants.In addition to the underlying approximations of ab-initio parameterization,two main challenges are associated with this path:high computational costs and lack of automation in the frameworks using this methodology,which affect the discovery rate of novel materials with ad-hoc properties.Here,the Automatic Anharmonic Phonon Library(AAPL)is presented.It efficiently computes interatomic force constants by making effective use of crystal symmetry analysis,it solves the Boltzmann transport equation to obtain κ_(l),and allows a fully integrated operation with minimum user intervention,a rational addition to the current high-throughput accelerated materials development framework AFLOW.An“experiment vs.theory”study of the approach is shown,comparing accuracy and speed with respect to other available packages,and for materials characterized by strong electron localization and correlation.Combining AAPL with the pseudo-hybrid functional ACBN0 is possible to improve accuracy without increasing computational requirements.展开更多
基金This work also used the Extreme Science and Engineering Discovery Environment(XSEDE)Stampede2 at the TACC through allocation TG-DMR190035.
文摘Chemical design of SiO_(2)-based glasses with high elastic moduli and low weight is of great interest.However,it is difficult to find a universal expression to predict the elastic moduli according to the glass composition before synthesis since the elastic moduli are a complex function of interatomic bonds and their ordering at different length scales.Here we show that the densities and elastic moduli of SiO_(2)-based glasses can be efficiently predicted by machine learning(ML)techniques across a complex compositional space with multiple(>10)types of additive oxides besides SiO_(2).Our machine learning approach relies on a training set generated by high-throughput molecular dynamic(MD)simulations,a set of elaborately constructed descriptors that bridges the empirical statistical modeling with the fundamental physics of interatomic bonding,and a statistical learning/predicting model developed by implementing least absolute shrinkage and selection operator with a gradient boost machine(GBM-LASSO).
基金support by the DOE(DE-AC02-05CH11231),specifically the Basic Energy Sciences program under Grant#EDCBEEpartial support by DOD-ONR(N00014-13-1-0635,N00014-11-1-0136,and N00014-15-1-2863)the Alexander von Humboldt Foundation for financial support(Fritz-Haber-Institut der Max-Planck-Gesellschaft,14195 Berlin-Dahlem,Germany).
文摘One of the most accurate approaches for calculating lattice thermal conductivity,κ_(l),is solving the Boltzmann transport equation starting from third-order anharmonic force constants.In addition to the underlying approximations of ab-initio parameterization,two main challenges are associated with this path:high computational costs and lack of automation in the frameworks using this methodology,which affect the discovery rate of novel materials with ad-hoc properties.Here,the Automatic Anharmonic Phonon Library(AAPL)is presented.It efficiently computes interatomic force constants by making effective use of crystal symmetry analysis,it solves the Boltzmann transport equation to obtain κ_(l),and allows a fully integrated operation with minimum user intervention,a rational addition to the current high-throughput accelerated materials development framework AFLOW.An“experiment vs.theory”study of the approach is shown,comparing accuracy and speed with respect to other available packages,and for materials characterized by strong electron localization and correlation.Combining AAPL with the pseudo-hybrid functional ACBN0 is possible to improve accuracy without increasing computational requirements.
