Atsuto Seko

Prediction of Low-Thermal-Conductivity Compounds with First-Principles Anharmonic Lattice-Dynamics Calculations and Bayesian Optimization.

Compounds of low lattice thermal conductivity (LTC) are essential for seeking thermoelectric materials with high conversion efficiency. Some strategies have been used to decrease LTC. However, such trials have yielded successes only within a limited exploration space. Here, we report the virtual screening of a library containing 54,779 compounds. Our strategy is to search the library through Bayesian optimization using for the initial data the LTC obtained from first-principles anharmonic lattice-dynamics calculations for a set of 101 compounds. We discovered 221 materials with very low LTC. Two of them even have an electronic band gap <1 eV, which makes them exceptional candidates for thermoelectric applications. In addition to those newly discovered thermoelectric materials, the present strategy is believed to be powerful for many other applications in which the chemistry of materials is required to be optimized.

Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single- and binary-component solids

A combination of systematic density-functional theory (DFT) calculations and machine learning techniques has a wide range of potential applications. This study presents an application of the combination of systematic DFT calculations and regression techniques to the prediction of the melting temperature for single and binary compounds. Here we adopt the ordinary least-squares regression, partial least-squares regression, support vector regression, and Gaussian process regression. Among the four kinds of regression techniques, SVR provides the best prediction. The inclusion of physical properties computed by the DFT calculation to a set of predictor variables makes the prediction better. In addition, limitation of the predictive power is shown when extrapolation from the training dataset is required. Finally, a simulation to find the highest melting temperature toward the efficient materials design using kriging is demonstrated. The kriging design finds the compound with the highest melting temperature much faster than random designs. This result may stimulate the application of kriging to efficient materials design for a broad range of applications.

Native defects in oxide semiconductors: a density functional approach

We report a semilocal and hybrid Hartree-Fock density functional study of native defects in three oxide semiconductors: ZnO, SrTiO(3), and SnO. The defect that is responsible for the n-type conductivity of ZnO has been debated, in which the O vacancy, Zn interstitial, their complexes, and residual H impurity are considered candidates. Our results indicate that the O vacancy induces a deep and localized in-gap state, whereas the Zn interstitial is a shallow donor and hence can be a source of the carriers. In view of the formation energies, the O vacancy is likely to form with a substantial concentration under O-poor conditions, but the Zn interstitial is unlikely. We thus propose that the O vacancy is relevant to the nonstoichiometry of ZnO and that a source other than the native defects, such as the H impurity, needs to be considered for the n-type conductivity. For SrTiO(3), the O vacancy and its complexes have been regarded as the origins of some of the remarkable electrical and optical properties. We suggest significant roles of the Ti antisite for a new insight into the defect-induced properties. Two types of Ti antisite, both of which are off-centered from the Sr site but toward different directions, exhibit low formation energies under Ti-rich conditions as does the O vacancy. They can explain optical properties such as visible-light emission, deep-level absorption, and the ferroelectricity observed in reduced SrTiO(3). As an example of p-type conductors, SnO has been investigated with a focus on the acceptor-like native defects. Under O-rich conditions, the Sn vacancy and O interstitial are found to be energetically favorable. The Sn vacancy induces shallow acceptor levels and can therefore be a source of carriers. The O interstitial shows no in-gap levels and hence it is inactive in terms of the carrier generation and compensation. However, this defect is a key to the understanding of the structures of intermediate compounds between SnO and SnO(2).

Prediction model of band gap for inorganic compounds by combination of density functional theory calculations and machine learning techniques

Machine learning techniques are applied to make prediction models of the

Theoretical Photovoltaic Conversion Efficiencies of ZnSnP2, CdSnP2, and Zn1-xCdxSnP2 Alloys

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Sparse representation for a potential energy surface

band gaps for 270 inorganic compounds using Kohn-Sham (KS) band gaps, cohesive energy, crystalline volume per atom, and other fundamental information of constituent elements as predictors. Ordinary least squares regression (OLSR), least absolute shrinkage and selection operator, and nonlinear support vector regression (SVR) methods are applied with two levels of predictor sets. When the KS band gap by generalized gradient approximation of Perdew-Burke-Ernzerhof (PBE) or modified Becke-Johnson (mBJ) is used as a single predictor, the OLSR model predicts the

Machine-learning-based selective sampling procedure for identifying the low-energy region in a potential energy surface: A case study on proton conduction in oxides

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Phonon softening in paramagnetic bcc Fe and its relationship to the pressure-induced phase transition

band gap of randomly selected test data with the root-mean-square error (RMSE) of 0.59 eV. When KS band gap by PBE and mBJ methods are used together with a set of predictors representing constituent elements and compounds, the RMSE decreases significantly. The best model by SVR yields the RMSE of 0.24 eV. Band gaps estimated in this way should be useful as predictors for virtual screening of a large set of materials.

First-principles molecular dynamics study for average structure and oxygen diffusivity at high temperature in cubic Bi2O3

The performances of ZnSnP2, CdSnP2, and Zn1-xCdxSnP2 alloys as solar cell photoabsorbers are assessed using photovoltaic conversion efficiency simulations in conjunction with first-principles calculations based on hybrid density functional theory. The band gap of Zn1-xCdxSnP2 decreases with increasing Cd content x and shows a small bowing. The electronic structure and optical absorption spectrum depend weakly on the composition, aside from the band gap and spectral threshold. The conversion efficiency is almost converged to the Shockley–Queisser limit at a photoabsorber thickness of a few micrometers for any composition of Zn1-xCdxSnP2, similarly to the cases of GaAs, CdTe, CuInSe2, and CuGaSe2.

Matrix- and tensor-based recommender systems for the discovery of currently unknown inorganic compounds

We propose a simple scheme to estimate potential energy surface (PES) with which the accuracy can be easily controlled and improved up to the level of the density functional theory (DFT) calculations. It is based on a model selection within the framework of linear regression using the least absolute shrinkage and selection operator (LASSO) technique. Basis functions are selected from a systematic large set of candidate functions. The sparsity of PES significantly reduces the computational demands for evaluation of the energy and the force in molecular dynamics simulations without losing the accuracy. The usefulness of the scheme is well demonstrated for describing elemental metals of Na and Mg.