Accelerated Materials Design of Lithium Superionic Conductors Based on First‐Principles Calculations and Machine Learning Algorithms

Fujimura, Kazuma; Seko, Akira; Koyama, Yukinori; Kuwabara, Akihide; Kishida, Ippei; Shitara, Kazuki; Fisher, Craig A. J.; Moriwake, Hiroki; Tanaka, Isao

doi:10.1002/aenm.201300060

Cited by 214 publications

(158 citation statements)

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“…Examples can be found in the literature (e.g., Refs. [1][2][3][4]). Other candidates are simply a binary digit representing the presence of each element in a compound ( Fig.…”

Section: Compound Descriptorsmentioning

confidence: 99%

Descriptors for Machine Learning of Materials Data

2018

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Descriptors, which are representations of compounds, play an essential role in machine learning of materials data. Although many representations of elements and structures of compounds are known, these representations are difficult to use as descriptors in their unchanged forms. This chapter shows how compounds in a dataset can be represented as descriptors and applied to machine-learning models for materials datasets.Keywords Machine-learning interatomic potential ⋅ Lattice thermal conductivity ⋅ Recommender system ⋅ Gaussian process ⋅ Bayesian optimization IntroductionRecent developments of data-centric approaches should accelerate the progress in materials science dramatically. Thanks to the recent advances in computational power and techniques, the results from numerous density functional theory (DFT) calculations with predictive performances have been stored as databases. A combination of such databases and an efficient machine-learning approach should realize prediction and classification models of target physical properties. Consequently, machine-learning techniques are becoming ubiquitous. They are used to explore materials and structures from a huge number of candidates and to extract meaningful information and patterns from existing data.A key factor in controlling the performance of a machine-learning approach is how compounds are represented in a data set. Representations of compounds are called "descriptors" or "features". To perform machine-learning modeling, available descriptors must be determined according to the evaluation cost of the target property

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“…Examples can be found in the literature (e.g., Refs. [1][2][3][4]). Other candidates are simply a binary digit representing the presence of each element in a compound ( Fig.…”

Section: Compound Descriptorsmentioning

confidence: 99%

Descriptors for Machine Learning of Materials Data

2018

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“…Recently, several attempts have been made to find an optimal functional material using high-throughput computational screening. [5][6][7][8] The machine-learning approach presented by G. Pilania 9 is a more accelerated approach to predict properties of large numbers of polymers; however, the prediction accuracy for E g and κ is low for extending the method to other systems. In this study, we perform ab initio calculations on~1800 oxides (except for 3d transition metal oxides) that cover most binary and ternary oxides identified to date and suggest novel candidate high-κ dielectrics suitable for each device type.…”

Section: Introductionmentioning

confidence: 99%

Novel high-κ dielectrics for next-generation electronic devices screened by automated ab initio calculations

et al. 2015

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As the scale of transistors and capacitors in electronics is reduced to less than a few nanometers, leakage currents pose a serious problem to the device's reliability. To overcome this dilemma, high-κ materials that exhibit a larger permittivity and band gap are introduced as gate dielectrics to enhance both the capacitance and block leakage simultaneously. Currently, HfO 2 is widely used as a high-κ dielectric; however, a higher-κ material remains desired for further enhancement. To find new high-κ materials, we conduct a high-throughput ab initio calculation for band gap and permittivity. The accurate and efficient calculation is enabled by newly developed automation codes that fully automate a series of delicate methods in a highly optimized manner. We can, thus, calculate 41800 structures of binary and ternary oxides from the Inorganic Crystal Structure Database and obtain a total property map. We confirm that the inverse correlation relationship between the band gap and permittivity is roughly valid for most oxides. However, new candidate materials exhibit interesting properties, such as large permittivity, despite their large band gaps. Analyzing these materials, we discuss the origin of large κ values and suggest design rules to find new high-κ materials that have not yet been discovered.

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“…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.…”

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

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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...

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Accelerated Materials Design of Lithium Superionic Conductors Based on First‐Principles Calculations and Machine Learning Algorithms

Cited by 214 publications

References 44 publications

Descriptors for Machine Learning of Materials Data

Descriptors for Machine Learning of Materials Data

Novel high-κ dielectrics for next-generation electronic devices screened by automated ab initio calculations

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

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