High-Throughput Screening of Extrinsic Point Defect Properties in Si and Ge: Database and Applications

Sluydts, Michael; Pieters, Michael; Vanhellemont, Jan; Speybroeck, Véronique Van; Cottenier, Stefaan

doi:10.1021/acs.chemmater.6b03368

Cited by 11 publications

(6 citation statements)

References 49 publications

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“…Ab initio HT computational methods are a powerful approach to identify new applicationspecific materials. 1,[8][9][10][11][12][13][14][15][16][17][18][19] Instead of investigating materials one at a time, such methods use algorithms to automate the calculations and analysis. However, the screening process can quickly require tremendous amounts of computational resources because (i) ab initio calculations, usually performed using density functional theory (DFT)-based methods, are computationally expensive, (ii) up to hundreds of DFT runs per compound can be required to compute some materials properties (e.g.…”

Section: Methodsmentioning

confidence: 99%

“…Ab initio HT computational methods are a powerful family of approaches to identify new application-specific materials. Recent papers made use of those approach to discover new materials for batteries, , thermo- − and ferroelectrics, and other applications. − Instead of investigating materials one at a time, such methods use algorithms to automate the calculations and analysis. However, the screening process can quickly require tremendous amounts of computational resources because (i) ab initio calculations, usually performed using density functional theory (DFT)-based methods, are computationally expensive, (ii) up to hundreds of DFT runs per compound can be required to compute some materials properties (e.g., anharmonic thermal conductivity , ), and (iii) the number of prospective candidates can easily climb into the hundreds of thousands.…”

Section: Methodsmentioning

confidence: 99%

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How Chemical Composition Alone Can Predict Vibrational Free Energies and Entropies of Solids

et al. 2017

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Computing vibrational free energies (F vib ) and entropies (S vib ) has posed a long standing challenge to the high-throughput ab initio investigation of finite temperature properties of solids. Here we use machine-learning techniques to efficiently predict F vib and S vib of crystalline compounds in the Inorganic Crystal Structure Database. By employing descriptors based simply on the chemical formula and using a training set of only 300 compounds, mean absolute errors of less than 0.04 meV/K/atom (15 meV/atom) are achieved for S vib (F vib ), whose values are distributed within a range of 0.9 meV/K/atom (300 meV/atom.) In addition, for training sets containing fewer than 2,000 compounds the chemical formula alone is shown to perform as well as, if not better than, four other more complex descriptors previously used in the literature. The accuracy and simplicity of the approach mean that it can be advantageously used for the fast screening of phase diagrams or chemical reactions at finite temperatures.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

How Chemical Composition Alone Can Predict Vibrational Free Energies and Entropies of Solids

et al. 2017

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“…Moreover, the understanding of processes and material properties will require the support of theory. In this context, the screening of extrinsic point defect properties in Si and Ge shown in the literature is a good example for the prediction of material properties, which helps to tailor a nanomaterial for a certain application . Furthermore, a very powerful tool to predict materials with interesting physical properties is machine learning. − However, big data are required to get accurate extrapolations via machine learning, which again points out the importance of a detailed characterization of nanomaterials.…”

Section: Discussionmentioning

confidence: 99%

Metastable Group IV Allotropes and Solid Solutions: Nanoparticles and Nanowires

2020

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In the past decades, group IV nanowires and nanoparticles have been the subject of extensive research. Beside tremendous progress in morphological control and integration in advanced device architectures, research on allotropes and metastable compositions has gained considerable interest. Several new approaches now allow the controlled formation of specific allotropes in the nanostructured form as well as chemical compositions not attainable by traditional synthesis protocols. The conditions applied to form these metastable solid solutions and allotropes are usually far from thermodynamic equilibrium or rely on unconventional templates. The increased interest in the field of metastable group IV nanostructures arises from their altered physical properties, including tunable, direct bandgaps with energies equivalent to the near- to mid-infrared spectral region as described for materials such as Ge1–x Sn x and hexagonal Si1–x Ge x . The implementation of these material characteristics in complementary metal oxide semiconductor (CMOS) processes are desirable for applications in electronics, optoelectronics, sensors, optics, etc. but also their use as nonsurface bound nanoparticles in sensing, nanobiotechnology and nanomedicine can offer additional opportunities. This review article highlights both the important advancements and still open questions for the continued development of these nanoscaled materials for next-generation device concepts.

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“…3b (elemental surface energies). Vacancy formation energies in semiconductors and insulators may be particularly difficult to correctly predict with FFs due to the existence of charge states [21][22][23] . The charge induced defect properties are captured well using DFT but can't be reproduced classically.…”

Section: Fig 2 Snapshot Of the Database Showing The Interactive Perio...mentioning

confidence: 99%

High-throughput assessment of vacancy formation and surface energies of materials using classical force-fields

Choudhary

Biacchi

Ghosh

et al. 2018

J. Phys.: Condens. Matter

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In this work, we present an open access database for surface and vacancy-formation energies using classical force-fields (FFs). These quantities are essential in understanding diffusion behavior, nanoparticle formation and catalytic activities. FFs are often designed for a specific application, hence, this database allows the user to understand whether a FF is suitable for investigating particular defect and surface-related material properties. The FF results are compared to density functional theory and experimental data whenever applicable for validation. At present, we have 17 506 surface energies and 1000 vacancy formation energies calculation in our database and the database is still growing. All the data generated, and the computational tools used, are shared publicly at the following websites: www.ctcms.nist.gov/~knc6/periodic.html, https://jarvis.nist.gov and https://github.com/usnistgov/jarvis. Approximations used during the high-throughput calculations are clearly mentioned. Using some of the example cases, we show how our data can be used to directly compare different FFs for a material and to interpret experimental findings such as using Wulff construction for predicting equilibrium shape of nanoparticles. Similarly, the vacancy formation energies data can be useful in understanding diffusion related properties.

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High-Throughput Screening of Extrinsic Point Defect Properties in Si and Ge: Database and Applications

Cited by 11 publications

References 49 publications

How Chemical Composition Alone Can Predict Vibrational Free Energies and Entropies of Solids

How Chemical Composition Alone Can Predict Vibrational Free Energies and Entropies of Solids

Metastable Group IV Allotropes and Solid Solutions: Nanoparticles and Nanowires

High-throughput assessment of vacancy formation and surface energies of materials using classical force-fields

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