Applications of DFT + DMFT in Materials Science

Paul, Arpita; Birol, Turan

doi:10.1146/annurev-matsci-070218-121825

Cited by 52 publications

(37 citation statements)

References 132 publications

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“…The GW approximation [63,64], and the solution of the Bethe-Salpeter equation for exciton dynamics [65,66], among other methods are famous examples. Moreover, strongly correlated phenomena, which is not captured by the standard DFT approach are now being investigated using the Dynamical Mean Field Theory (DMFT) [67,68]. Which can be integrated into the DFT self-consistent cycle [69], or used in postprocessing level [70].…”

Section: Current Statusmentioning

confidence: 99%

From DFT to machine learning: recent approaches to materials science–a review

et al. 2019

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Recent advances in experimental and computational methods are increasing the quantity and complexity of generated data. This massive amount of raw data needs to be stored and interpreted in order to advance the materials science field. Identifying correlations and patterns from large amounts of complex data is being performed by machine learning algorithms for decades. Recently, the materials science community started to invest in these methodologies to extract knowledge and insights from the accumulated data. This review follows a logical sequence starting from density functional theory as the representative instance of electronic structure methods, to the subsequent high-throughput approach, used to generate large amounts of data. Ultimately, data-driven strategies which include data mining, screening, and machine learning techniques, employ the data generated. We show how these approaches to modern computational materials science are being used to uncover complexities and design novel materials with enhanced properties. Finally, we point to the present research problems, challenges, and potential future perspectives of this new exciting field. characterized by its diverse and huge volume, usually ranging from terabytes to petabytes of data, being created in or near real-time. Such data is found either structured and unstructured in nature, and is exhaustive, usually aiming to capture entire populations in a scalable manner [7]. Simple tasks represent challenges in this scale: capture, curation, storage, search, sharing, analysis, and visualization of the data cannot be accomplished without the proper tools. Thus, it can be effectively summarized by the popular 'five V's': volume, velocity, variety, veracity, and value, shown in figure 3(right). A related sixth V is visualization, although not exclusive to Big Data, which requires different techniques to handle data with various characteristics.Striving to tackle the challenges imposed by Big Data, the field of Data Science has arisen. It is largely interdisciplinary being a combination of mathematics and statistics, computer science and programming, and domain knowledge for problem definition and solving, as shown in figure 3(left). Its objective is, roughly speaking, to deal with the whole process of data production, cleaning, preparation, and finally, analysis. Data science encompasses areas such as Big Data, which deals with large volumes of data, and data mining, which relates to analysis processes to discover patterns and extract knowledge from data, part of the so-called Knowledge Discovery in Databases (KDD).The analysis process within Data Science is challenging, as the techniques are very different from traditional static and rigid datasets, generated and analyzed under a predetermined hypothesis. The distinction from traditional data is based on the larger abundance, exhaustivity, and variety of Big Data. It is also much more dynamic, messy and uncertain, being highly relational [7]. Recently, the possibility of overcoming such a challenge slowly sta...

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Section: Current Statusmentioning

confidence: 99%

From DFT to machine learning: recent approaches to materials science–a review

et al. 2019

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“…To show the superior timing of the SDR fitting procedure, we compare it to the BFGS algorithm [50] as supported by the fminunc routine in MATLAB and the BOBYQA method (a derivativefree optimization method) implemented in the nlopt library [54,55] applied directly to Eq. (11). The timings in seconds for a number of SDR, BFGS, and BOBYQA fits are reported in Tab.…”

Section: Timingsmentioning

confidence: 99%

“…Some of the most prominent examples of embedding techniques are dynamical mean-field theory (DMFT) [1][2][3], density-matrix embedding theory (DMET) [4], and self-energy embedding theory (SEET) [5] among many others. These methods have been widely successful in unveiling the low energy properties of model Hamiltonians and real materials alike [6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Efficient hybridization fitting for dynamical mean-field theory via semi-definite relaxation

et al. 2020

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We introduce a nested optimization procedure using semi-definite relaxation for the fitting step in Hamiltonian-based cluster dynamical mean-field theory (DMFT) methodologies. We show that the proposed method is more efficient and flexible than state-of-the-art fitting schemes, which allows us to treat as large a number of bath sites as the impurity solver at hand allows. We characterize its robustness to initial conditions and symmetry constraints, thus providing conclusive evidence that in the presence of a large bath, our semi-definite relaxation approach can find the correct set of bath parameters without needing to include a priori knowledge of the properties that are to be described. We believe this method will be of great use for Hamiltonian-based calculations, simplifying and improving one of the key steps in cluster dynamical mean-field theory calculations. arXiv:1907.07191v2 [cond-mat.str-el] 27 Aug 2019 1. Relax the ansatz in Eq. (9) by replacing the rank-one matrices V V * by positive semidefinite matrices X of arbitrary rank, resulting in a new loss function, 2. Optimize the poles and the matrices in an alternating fashion, which leverages state-ofthe-art conic programming and quasi-Newton methods, and

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“…In order to predict the electronic structure of Sr 2 VNbO 6 at room temperature, we performed DFT + embedded dynamical mean field theory (DFT+eDMFT) calculations [18,19]. A simple measure of electronic correlation strength, especially for correlated metals that are close to a Fermi liquid phase, is the mass renormalization factor Z, which gives the ratio of the effective mass m * of the electrons to the band mass m b calculated from DFT, and is calculated from the slope of the DMFT electronic self-energy near the Fermi level as…”

Section: (F)]mentioning

confidence: 99%

“…Both parent compounds of Sr 2 VNbO 6 (SrVO 3 and SrNbO 3 ) are metallic perovskites with a single electron in partially filled d shells of their B-site cations (V 4+ and Nb 4+ ) [17]. Using density functional theory + embedded dynamical mean field theory (DFT+DMFT) [18][19][20], we show that this double perovskite has an electronic structure that is strongly intertwined with the cation order and crystal structural parameters (bond-length disproportionation) in an exceedingly sensitive fashion. Even though V and Nb come from the same group in the Periodic Table, the difference between their electronegativities leads to an almost complete transfer of an electron from Nb to V in Sr 2 VNbO 6 , which results in formal valences closer to Nb 5+ and V 3+ instead of Nb 4+ and V 4+ .…”

Section: Introductionmentioning

confidence: 99%

Cation order control of correlations in double perovskite Sr2VNbO6

Paul

Birol

2020

Phys. Rev. Research

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Double perovskites extend the design space for new materials and they often host phenomena that do not exist in their parent perovskite compounds. Here we present a detailed first-principles study of the correlated double perovskite Sr 2 VNbO 6 , where the intercationic charge transfer and strength of electronic correlations depend strongly on the cation order. By using density functional theory + embedded dynamical mean field theory, we show that this compound has a completely different electronic structure than either of its parent compounds despite V and Nb being from the same group in the Periodic Table. We explain how the electronic correlations' effect on the crystal structural parameters determines on which side of the Hund's metal-Mott insulator transition the material is. Our results demonstrate the emergence of Hund's metallic behavior in a double perovskite that has d 1 parents and underline the importance of electronic correlation effects on the crystal structure.

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Applications of DFT + DMFT in Materials Science

Cited by 52 publications

References 132 publications

From DFT to machine learning: recent approaches to materials science–a review

From DFT to machine learning: recent approaches to materials science–a review

Efficient hybridization fitting for dynamical mean-field theory via semi-definite relaxation

Cation order control of correlations in double perovskite Sr2VNbO6

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