AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations

Curtarolo, Stefano; Setyawan, Wahyu; Wang, Shidong; Xue, Junkai; Yang, Kesong; Taylor, Richard; Hart, Gus L. W.; Sanvito, Stefano; Nardelli, Marco Buongiorno; Mingo, Natalio; Levy, Ohad

doi:10.1016/j.commatsci.2012.02.002

Cited by 918 publications

(759 citation statements)

References 26 publications

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“…We adopt kriging method based on the Gaussian process regression (GPR) [25,26] of LTC simply using two physical quantities, V and ρ, as predictors. These quantities are available in most of the experimental or computational crystal structure database, such as ICSD [27], Atomwork [28], Materials Project Database (MPD) [29], and aflowlib [30]. Although a phenomenological relationship has been proposed between log κ L and V [24], the correlation between them is not so high.…”

Section: (C)mentioning

confidence: 99%

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

et al. 2015

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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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Section: (C)mentioning

confidence: 99%

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

et al. 2015

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“…As a result, high-throughput explorations of the vast chemical space are increasingly being pursued and have significantly aided our intuition and knowledge-base of material properties (Ceder et al, 2011;Jain et al, 2011;Yu and Zunger, 2012;Curtarolo et al, 2013;Pilania et al, 2013Pilania et al, , 2016Sharma et al, 2014;Balachandran et al, 2016;Kim et al, 2016;Mannodi-Kanakkithodi et al, 2016). Massive open source databases of materials properties (including electronic structure, thermodynamic, and structural properties) are now available on the web (Curtarolo et al, 2012;Computational Materials Repository, 2015;Materials Project -A Materials Genome Approach, 2015). Big-data materials infrastructure (Service, 2012) is increasingly being built with the intent of knowledge extraction and rule-mining to identify candidate materials for next-generation materials breakthroughs.…”

Section: Introductionmentioning

confidence: 99%

Finding New Perovskite Halides via Machine Learning

et al. 2016

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Advanced materials with improved properties have the potential to fuel future technological advancements. However, identification and discovery of these optimal materials for a specific application is a non-trivial task, because of the vastness of the chemical search space with enormous compositional and configurational degrees of freedom. Materials informatics provides an efficient approach toward rational design of new materials, via learning from known data to make decisions on new and previously unexplored compounds in an accelerated manner. Here, we demonstrate the power and utility of such statistical learning (or machine learning, henceforth referred to as ML) via building a support vector machine (SVM) based classifier that uses elemental features (or descriptors) to predict the formability of a given ABX 3 halide composition (where A and B represent monovalent and divalent cations, respectively, and X is F, Cl, Br, or I anion) in the perovskite crystal structure. The classification model is built by learning from a dataset of 185 experimentally known ABX 3 compounds. After exploring a wide range of features, we identify ionic radii, tolerance factor, and octahedral factor to be the most important factors for the classification, suggesting that steric and geometric packing effects govern the stability of these halides. The trained and validated models then predict, with a high degree of confidence, several novel ABX 3 compositions with perovskite crystal structure.

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“…High-throughput computing and materials databases (1)(2)(3), largely based on density-functional theory (DFT), have recently enabled rapid screening of solid-state compounds with simulation for multiple properties and functionalities (4)(5)(6)(7)(8)(9)(10). Since their advent just a few years ago, these DFT-based databases and analytics tools have already been used to identify more than 20 new functional materials that were later confirmed by experiments across a number of applications (8), motivating concerted efforts to validate theory predictions with experiments (11).…”

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confidence: 99%

Solar fuels photoanode materials discovery by integrating high-throughput theory and experiment

Yan

Suram

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

173

177

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The limited number of known low-band-gap photoelectrocatalytic materials poses a significant challenge for the generation of chemical fuels from sunlight. Using high-throughput ab initio theory with experiments in an integrated workflow, we find eight ternary vanadate oxide photoanodes in the target band-gap range (1.2-2.8 eV). Detailed analysis of these vanadate compounds reveals the key role of VO 4 structural motifs and electronic band-edge character in efficient photoanodes, initiating a genome for such materials and paving the way for a broadly applicable high-throughput-discovery and materials-by-design feedback loop. Considerably expanding the number of known photoelectrocatalysts for water oxidation, our study establishes ternary metal vanadates as a prolific class of photoanode materials for generation of chemical fuels from sunlight and demonstrates our high-throughput theory-experiment pipeline as a prolific approach to materials discovery. solar fuels materials | density-functional theory | high-throughput experiments | complex oxides | photocatalysis T he use of predictive simulation in combination with experiments for the accelerated discovery and rational design of functional materials is a challenge of significant contemporary interest. High-throughput computing and materials databases (1-3), largely based on density-functional theory (DFT), have recently enabled rapid screening of solid-state compounds with simulation for multiple properties and functionalities (4-10). Since their advent just a few years ago, these DFT-based databases and analytics tools have already been used to identify more than 20 new functional materials that were later confirmed by experiments across a number of applications (8), motivating concerted efforts to validate theory predictions with experiments (11). However, in photoelectrochemistry for the renewable synthesis of solar fuels, efficient metal-oxide photoanode materials--photoelectrocatalysts for the oxygen evolution reaction (OER)--remain critically missing (12). Forty years of experimental research has yielded just 16 metal-oxide photoanode compounds with band-gap energy in the desirable 1.2-2.8-eV range that strongly overlaps with the solar spectrum. Prior high-throughput computational screening studies have yet to expand this list (6, 7, 13), in part due to quantitative limitations in predictability of the electronic structure--especially band-gap energy, E g , and the valence band maximum (VBM) energy, E VBM --from the chemical composition and crystal structure. Our integration of ab initio theory with high-throughput experiments has yielded a most prolific materials discovery effort, as demonstrated by the identification of 12 water oxidation photoelectrocatalysts in the target band-gap range, including our recently reported 4 copper vanadates (14) and 8 additional metal vanadates reported here.Monoclinic BiVO 4 (15) has received substantial attention as a solar fuels photoanode material due to its promising OER photoactivity. It has a desirable 2.4-eV band...

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AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations

Cited by 918 publications

References 26 publications

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

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

Finding New Perovskite Halides via Machine Learning

Solar fuels photoanode materials discovery by integrating high-throughput theory and experiment

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