PAOFLOW: A utility to construct and operate on ab initio Hamiltonians from the projections of electronic wavefunctions on atomic orbital bases, including characterization of topological materials

Nardelli, Marco Buongiorno; Cerasoli, Frank; Costa, Marcio; Curtarolo, Stefano; Gennaro, Riccardo De; Fornari, Marco; Liyanage, Laalitha; Supka, Andrew; Wang, Haihang

doi:10.1016/j.commatsci.2017.11.034

Cited by 86 publications

(77 citation statements)

References 20 publications

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“…For example, quantities that require large reciprocal space sampling, such as electrical conductivity, spin Hall conductivity (SHC), Anomalous Hall conductivity (AHC), to cite a few, are cumbersome to obtain. The electrical conductivity can be calculated using interpolation methods based on DFT calculations implemented in BOLTZTRAP, BOLTZWANN, SHENGBTE, and PAOFLOW [74][75][76][77]. PAOFLOW can also calculate SHC, AHC, Fermi surfaces, topological invariants, and other properties.…”

Section: Current Statusmentioning

confidence: 99%

“…Nevertheless, its computation is extremely costly, since it requires a fine sampling of the reciprocal space [341,342]. Only recently HT investigations of thermoelectric materials were feasible, owing to interpolation schemes capable of circumventing the computational cost [74][75][76][77]. Wang et al calculated the thermoelectric properties of ≈2500 materials, finding several large power factor materials.…”

Section: Materials Discovery Design and Characterizationmentioning

confidence: 99%

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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%

Section: Materials Discovery Design and Characterizationmentioning

confidence: 99%

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

et al. 2019

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“…Ionic cores are described using projector augmented wave (PAW) pseudopotentials [39]. The local effective paramagnetic Hamiltonian is obtained using a direct projection of the Kohn-Sham states onto a pseudoatomic orbital (PAO) basis [40] as implemented in the PAOFLOW code [41].…”

Section: Methodsmentioning

confidence: 99%

Nonreciprocal magnons in a two-dimensional crystal with out-of-plane magnetization

et al. 2020

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Nonreciprocal spin waves have a chiral asymmetry so that their energy is different for two opposite wave vectors. They are found in atomically thin ferromagnetic overlayers with in-plane magnetization and are linked to the antisymmetric Dzyaloshinskii-Moriya surface exchange. We use an itinerant fermion theory based on first-principles calculations to predict that nonreciprocal magnons can occur in Fe 3 GeTe 2 , the first stand-alone metallic two-dimensional crystal with out-of-plane magnetization. We find that both the energy and lifetime of magnons are nonreciprocal, and we predict that acoustic magnons can have lifetimes up to hundreds of picoseconds, orders of magnitude larger than in other conducting magnets.

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“…In this work the p n threshold is set to 0.95. This methodology has been successful successfully applied to different materials and properties for a full description see references [20][21][22][23][24].…”

Section: Methodsmentioning

confidence: 99%

“…Spin-orbit coupling effect is essential to describe the topological quantum states in TI. In our calculations the SOC is introduced in two different ways: standard fullyrelativistic self-consistent DFT calculations [25] and via an effective approximation in the TB Hamiltonian [24,26]. The effective SOC included in the TB Hamiltonian can be written as…”

Section: Methodsmentioning

confidence: 99%

Controlling Topological States in Topological/Normal Insulator Heterostructures

Costa

Freitas

et al. 2018

ACS Omega

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We have performed a systematic investigation of the nature of the nontrivial interface states in topological/normal insulator (TI/NI) heterostructures. On the basis of first principles and a recently developed scheme to construct ab initio effective Hamiltonian matrices from density functional theory calculations, we studied systems of realistic sizes with high accuracy and control over the relevant parameters such as TI and NI band alignment, NI gap, and spin–orbit coupling strength. Our results for IV–VI compounds show the interface gap tunability by appropriately controlling the NI thickness, which can be explored for device design. Also, we verified the preservation of an in-plane spin texture in the interface-gaped topological states.

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PAOFLOW: A utility to construct and operate on ab initio Hamiltonians from the projections of electronic wavefunctions on atomic orbital bases, including characterization of topological materials

Cited by 86 publications

References 20 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

Nonreciprocal magnons in a two-dimensional crystal with out-of-plane magnetization

Controlling Topological States in Topological/Normal Insulator Heterostructures

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