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作者： Yong-Jie Hu Ge Zhao Mingfei Zhang Bin Bin Tyler Del Rose Qian Zhao Qun Zu Yang Chen Xuekun Sun Maarten de Jong Liang Qi Department of Materials Science and Engineering University of MichiganAnn ArborMI 48109USA Department of Statistics Pennsylvania State UniversityState CollegePA 16802USA Fariborz Maseeh Department of Mathematics and Statistics Portland State UniversityPortlandOR 97207USA Sinoma Science&Technology Co. Ltd.210012 NanjingJiangsuChina Continental Technology LLC IndianapolisIN 46033USA Department of Materials Science and Engineering University of CaliforniaBerkeleyCA 94720USA Space Exploration Technologies(spacex) HawthorneCA 90250USA

Chemical design of SiO_(2)-based glasses with high elastic moduli and low weight is of great ***,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 *** 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).

关键词： glasses moduli bridges

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An efficient and accurate framework for calculating lattice thermal conductivity of solids:AFLOW-AAPL Automatic Anharmonic Phonon Library

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npj Computational Materials 2017年第1期3卷 79-88页

作者： Jose J.Plata Pinku Nath Demet Usanmaz Jesus Carrete Cormac Toher Maarten de Jong Mark Asta Marco Fornari Marco Buongiorno Nardelli Stefano Curtarolo Department of Mechanical Engineering and Materials Science Duke UniversityDurhamNC 27708USA Institute of Materials Chemistry TU WienA-1060 ViennaAustria Department of Materials Science and Engineering University of CaliforniaBerkeley210 Hearst Memorial Mining BuildingBerkeleyUSA spacex 1 Rocket RoadHawthorneCA 90250USA Department of Physics and Science of Advanced Materials Program Central Michigan UniversityMount PleasantMI 48859USA Department of Physics and Department of Chemistry University of North TexasDentonTXUSA Materials Science Electrical EngineeringPhysics and ChemistryDuke UniversityDurhamNC 27708USA

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 *** 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 ***,the Automatic Anharmonic Phonon Library(AAPL)is *** 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 ***“experiment ***”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 *** AAPL with the pseudo-hybrid functional ACBN0 is possible to improve accuracy without increasing computational requirements.

关键词： properties. harmonic calculating

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