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 have even an electronic band gap < 1 eV, what 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 chemistry of materials are required to be optimized.Thermoelectric generators are essential for utilizing otherwise waste heat. Because of the technological importance, researchers have been seeking materials with high conversion efficiency for decades [1][2][3][4]. Compounds of low lattice thermal conductivity (LTC) are essential for this purpose. Different strategies have been used to decrease LTC. Recently, high throughput screening (HTS) of materials using materials database constructed by first principles calculations has been recognized as an efficient tool for accelerated materials discovery [5][6][7][8][9]. Thanks to the recent progress of computational power and techniques, a large set of first principles calculations can be performed with the accuracy comparable to experiments. This is a straightforward strategy when both of the following conditions are satisfied: 1) the target physical property can be accurately computed by first principles methods. 2) The exploration space is well defined and not too large to compute the target physical property exhaustively in the space.In order to evaluate LTC with the accuracy comparable to experimental data, however, we need to develop a method that is far beyond the ordinary density functional theory (DFT) calculations. Since we need to treat multiple interactions among phonons, or anharmonic lattice dynamics, the computational cost is many orders of magnitudes higher than the ordinary DFT calculations. Such expensive calculations are practically possible only for a small number of simple compounds. HTS of a large DFT database of LTC is not a realistic approach unless the exploration space is narrowly confined. In the year 2014, Carrete and coworkers concentrated their efforts to search low LTC materials within half-Heusler compounds [10]. They made HTS of wide variety of halfHeusler compounds by examination of thermodynamical stability via DFT results. Then LTC was estimated either by full first principles calculations or by a machinelearning algorithm for a selected small number of compounds. HTS of low LTC using a quasiharmonic Debye model was also reported in 2014 [11]. Efficient prediction of LTC throu...

First-principles calculations are made for five Ga2O3 polymorphs. The structure of ε-Ga2O3 with the space group Pna 21 (No. 33, orthorhombic), which is sometimes called κ-Ga2O3 in the literature, is consistent with experimental reports. The structure of γ-Ga2O3 is optimized within 14 inequivalent configurations of defective spinel structures. Phonon dispersion curves of four polymorphs are obtained. The volume expansivity, bulk modulus, and specific heat at constant volume are computed as a function of temperature within the quasi-harmonic approximation. The Helmholtz free energies of the polymorphs are thus compared. The expansivity shows a relationship of β<ε<α<δ, while β<ε<δ<α for the bulk modulus. The formation free energies have the tendency β<ε<α<δ<γ at low temperatures. With the increase of temperature, the difference in free energy between the β-phase and the ε-phase becomes smaller. Eventually the ε phase becomes more stable at above 1600 K.

The PHENIX detector is designed to perform a broad study of A-A, p-A, and p-p collisions to investigate nuclear matter under extreme conditions. A wide variety of probes, sensitive to all timescales, are used to study systematic variations with species and energy as well as to measure the spin structure of the nucleon. Designing for the needs of the heavy-ion and polarized-proton programs has produced a detector with unparalleled capabilities. PHENIX measures electron and muon pairs, photons, and hadrons with excellent energy and momentum resolution. The detector consists of a large number of subsystems that are discussed in other papers in this volume. The overall design parameters of the detector are presented. The PHENIX detector is designed to perform a broad study of A-A, p-A, and p-p collisions to investigate nuclear matter under extreme conditions. A wide variety of probes, sensitive to all timescales, are used to study systematic variations with species and energy as well as to measure the spin structure of the nucleon. Designing for the needs of the heavy-ion and polarized-proton programs has produced a detector with unparalleled capabilities. PHENIX measures electron and muon pairs, photons, and hadrons with excellent energy and momentum resolution. The detector consists of a large number of subsystems that are discussed in other papers in this volume. The overall design parameters of the detector are presented. Disciplines Engineering Physics | Physics Comments This is a manuscript of an article from Nuclear Instruments and Methods in Physics Research

The representations of a compound, called "descriptors" or "features", play an essential role in constructing a machine-learning model of its physical properties. In this study, we adopt a procedure for generating a systematic set of descriptors from simple elemental and structural representations. First it is applied to a large dataset composed of the cohesive energy for about 18000 compounds computed by density functional theory (DFT) calculation. As a result, we obtain a kernel ridge prediction model with a prediction error of 0.041 eV/atom, which is close to the "chemical accuracy" of 1 kcal/mol (0.043 eV/atom). The procedure is also applied to two smaller datasets, i.e., a dataset of the lattice thermal conductivity (LTC) for 110 compounds computed by DFT calculation and a dataset of the experimental melting temperature for 248 compounds. We examine the performance of the descriptor sets on the efficiency of Bayesian optimization in addition to the accuracy of the kernel ridge regression models. They exhibit good predictive performances.