A general-purpose machine learning framework for predicting properties of inorganic materials

A very active area of materials research is to devise methods that use machine learning to automatically extract predictive models from existing materials data.While prior examples have demonstrated successful models for some applications,many more applications exist where machine learning can make a strong impact.To enable faster development of machine-learning-based models for such applications,we have created a framework capable of being applied to a broad range of materials data.Our method works by using a chemically diverse list of attributes,which we demonstrate are suitable for describing a wide variety of properties,and a novel method for partitioning the data set into groups of similar materials to boost the predictive accuracy.In this manuscript,we demonstrate how this new method can be used to predict diverse properties of crystalline and amorphous materials,such as band gap energy and glass-forming ability.

Computational strategies for design and discovery of nanostructured thermoelectrics

The contribution of theoretical calculations and predictions in the development of advanced high-performance thermoelectrics has been increasingly significant and has successfully guided experiments to understand as well as achieve record-breaking results.In this review,recent developments in high-performance nanostructured bulk thermoelectric materials are discussed from the viewpoint of theoretical calculations.An effective emerging strategy for boosting thermoelectric performance involves minimizing electron scattering while maximizing heat-carrying phonon scattering on many length scales.We present several important strategies and key examples that highlight the contributions of first-principles-based calculations in revealing the intricate but tractable relationships for this synergistic optimization of thermoelectric performance.The integrated optimization approach results in a fourfold design strategy for improved materials:(1)a significant reduction of the lattice thermal conductivity through multiscale hierarchical architecturing,(2)a large enhancement of the Seebeck coefficient through intramatrix electronic band convergence engineering,(3)control of the carrier mobility through band alignment between the host and second phases,and(4)design of intrinsically low-thermal-conductivity materials by maximizing vibrational anharmonicity and acoustic-mode Gruneisen parameters.These combined effects serve to enhance the power factor while reducing the lattice thermal conductivity.This review provides an improved understanding of how theory is impacting the current state of this field and helps to guide the future search for high-performance thermoelectric materials.

Violation of the T^(−1) Relationship in the Lattice Thermal Conductivity of Mg_(3)Sb_(2) with Locally Asymmetric Vibrations

Most crystalline materials follow the guidelines of T^(-1) temperature-dependent lattice thermal conductivity(κ_(L))at elevated temperatures.Here,we observe a weak temperature dependence ofκL in Mg_(3)Sb_(2),T^(-0:48) from theory and T-0:57 from measurements,based on a comprehensive study combining ab initio molecular dynamics calculations and experimental measurements on single crystal Mg_(3)Sb_(2).These results can be understood in terms of the so-called“phonon renormalization”effects due to the strong temperature dependence of the interatomic force constants(IFCs).The increasing temperature leads to the frequency upshifting for those low-frequency phonons dominating heat transport,and more importantly,the phononphonon interactions are weakened.In-depth analysis reveals that the phenomenon is closely related to the temperature-induced asymmetric movements of Mg atoms within MgSb_(4) tetrahedron.With increasing temperature,these Mg atoms tend to locate at the areas with relatively low force in the force profile,leading to reduced effective 3^(rd)-order IFCs.The locally asymmetrical atomic movements at elevated temperatures can be further treated as an indicator of temperature-induced variations of IFCs and thus relatively strong phonon renormalization.The present work sheds light on the fundamental origins of anomalous temperature dependence of κ_(L) in thermoelectrics.