362 research outputs found
Convergence and pitfalls of density functional perturbation theory phonons calculations from a high-throughput perspective
The diffusion of large databases collecting different kind of material
properties from high-throughput density functional theory calculations has
opened new paths in the study of materials science thanks to data mining and
machine learning techniques. Phonon calculations have already been employed
successfully to predict materials properties and interpret experimental data,
e.g. phase stability, ferroelectricity and Raman spectra, so their availability
for a large set of materials will further increase the analytical and
predictive power at hand. Moving to a larger scale with density functional
perturbation calculations, however, requires the presence of a robust framework
to handle this challenging task. In light of this, we automatized the phonon
calculation and applied the result to the analysis of the convergence trends
for several materials. This allowed to identify and tackle some common problems
emerging in this kind of simulations and to lay out the basis to obtain
reliable phonon band structures from high-throughput calculations, as well as
optimizing the approach to standard phonon simulations
MODNet -- accurate and interpretable property predictions for limited materials datasets by feature selection and joint-learning
In order to make accurate predictions of material properties, current
machine-learning approaches generally require large amounts of data, which are
often not available in practice. In this work, an all-round framework is
presented which relies on a feedforward neural network, the selection of
physically-meaningful features and, when applicable, joint-learning. Next to
being faster in terms of training time, this approach is shown to outperform
current graph-network models on small datasets. In particular, the vibrational
entropy at 305 K of crystals is predicted with a mean absolute test error of
0.009 meV/K/atom (four times lower than previous studies). Furthermore,
joint-learning reduces the test error compared to single-target learning and
enables the prediction of multiple properties at once, such as temperature
functions. Finally, the selection algorithm highlights the most important
features and thus helps understanding the underlying physics.Comment: 5 pages, 2 figure
Influence of the "second gap" on the transparency-conductivity compromise in transparent conducting oxides: an ab initio study
Transparent conducting oxides (TCOs) are essential to many technologies.
These materials are doped (\emph{n}- or \emph{p}-type) oxides with a large
enough band gap (ideally 3~eV) to ensure transparency. However, the high
carrier concentration present in TCOs lead additionally to the possibility for
optical transitions from the occupied conduction bands to higher states for
\emph{n}-type materials and from lower states to the unoccupied valence bands
for \emph{p}-type TCOs. The "second gap" formed by these transitions might
limit transparency and a large second gap has been sometimes proposed as a
design criteria for high performance TCOs. Here, we study the influence of this
second gap on optical absorption using \emph{ab initio} computations for
several well-known \emph{n}- and \emph{p}-type TCOs. Our work demonstrates that
most known \emph{n}-type TCOs do not suffer from second gap absorption in the
visible even at very high carrier concentrations. On the contrary,
\emph{p}-type oxides show lowering of their optical transmission for high
carrier concentrations due to second gap effects. We link this dissimilarity to
the different chemistries involved in \emph{n}- versus typical \emph{p}-type
TCOs. Quantitatively, we show that second gap effects lead to only moderate
loss of transmission (even in p-type TCOs) and suggest that a wide second gap,
while beneficial, should not be considered as a needed criteria for a working
TCO.Comment: 6 pages, 4 figures, APS March Meetin
High-throughput data mined prediction of inorganic compounds and computational discovery of new lithium-ion battery cathode materials
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Materials Science and Engineering, 2011.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from PDF version of thesis.Includes bibliographical references (p. 117-129).The ability to computationally predict the properties of new materials, even prior to their synthesis, has been made possible due to the current accuracy of modern ab initio techniques. In some cases, high-throughput computations can be used to create large data sets of potential compounds and their computed properties. However, regardless of the field of application, such a computational high-throughput approach faces a major problem: to be relevant, the properties need to be computed on compounds (i.e., stoichiometries and crystal structures) that will be stable enough to be synthesized. In this thesis, we address this compound prediction problem through a combination of data mining and high-throughput Density Functional Theory. We first describe a method based on correlations between crystal structure prototypes that can be used with a limited computational budget to search for new ternary oxides. In addition, for the treatment of sparser data regions such as quaternaries, a new algorithm based on the data mining of ionic substitutions is proposed and analyzed. The second part of this thesis demonstrates the application of this highthroughput ab initio computing technique to the lithium-ion battery field. Here, we describe a large-scale computational search for novel cathode materials with specific battery properties, which enables experimentalists to focus on only the most promising chemistries. Finally, to illustrate the potential of new compound computational discovery using this approach, a novel chemical class of cathode materials, the carbonophosphates, is presented along with synthesis and electrochemical results.by Geoffroy Hautier.Ph.D
First-principles study of intrinsic and hydrogen point defects in the earth-abundant photovoltaic absorber Zn3P2
Zinc phosphide (Zn3P2) has had a long history of scientific interest largely
because of its potential for earth-abundant photovoltaics. To realize
high-efficiency Zn3P2 solar cells, it is critical to understand and control
point defects in this material. Using hybrid functional calculations, we assess
the energetics and electronic behavior of intrinsic point defects and hydrogen
impurities in Zn3P2. All intrinsic defects are found to act as compensating
centers in p-type Zn3P2 and have deep levels in the band gap, except for zinc
vacancies which are shallow acceptors and can act as a source of doping. Our
work highlights that zinc vacancies rather than phosphorus interstitials are
likely to be the main source of p-type doping in as-grown Zn3P2. We also show
that Zn-poor and P-rich growth conditions, which are usually used for enhancing
p-type conductivity of Zn3P2, will facilitate the formation of certain
deep-level defects (P_Zn and P_i) which might be detrimental to solar cell
efficiency. For hydrogen impurities, which are frequently present in the growth
environment of Zn3P2, we study interstitial hydrogen and hydrogen complexes
with vacancies. The results suggest small but beneficial effects of hydrogen on
the electrical properties of Zn3P2
High-Throughput Identification of Electrides from all Known Inorganic Materials
In this paper, we present the results of a large-scale, high-throughput
computational search for electrides among all known inorganic materials.
Analyzing a database of density functional theory results on more than 60,000
compounds, we identify 69 new electride candidates. We report on all these
candidates and discuss the structural and chemical factors leading to electride
formation. Among these candidates, our work identifies the first
partially-filled 3d transition metal containing electrides Ba3CrN3 and Sr3CrN3;
an unexpected finding that contravenes conventional chemistry.Comment: 5 page manuscript in letter format, 27 page Supplementary Informatio
Low-Dimensional Transport and Large Thermoelectric Power Factors in Bulk Semiconductors by Band Engineering of Highly Directional Electronic States
Thermoelectrics are promising to address energy issues but their exploitation
is still hampered by low efficiencies. So far, much improvement has been
achieved by reducing the thermal conductivity but less by maximizing the power
factor. The latter imposes apparently conflicting requirements on the band
structure: a narrow energy distribution and a low effective mass. Quantum
confinement in nanostructures or the introduction of resonant states were
suggested as possible solutions to this paradox but with limited success. Here,
we propose an original approach to fulfill both requirements in bulk
semiconductors. It exploits the highly-directional character of some orbitals
to engineer the band-structure and produce a type of low-dimensional transport
similar to that targeted in nanostructures, while retaining isotropic
properties. Using first-principles calculations, the theoretical concept is
demonstrated in FeYZ Heusler compounds, yielding power factors 4-5 times
larger than in classical thermoelectrics at room temperature. Our findings are
totally generic and rationalize the search of alternative compounds with a
similar behavior. Beyond thermoelectricity, these might be relevant also in the
context of electronic, superconducting or photovoltaic applications.Comment: 6 pages, 2 figure
From the computer to the laboratory: materials discovery and design using first-principles calculations
The development of new technological materials has historically been a difficult and time-consuming task. The traditional role of computation in materials design has been to better understand existing materials. However, an emerging paradigm for accelerated materials discovery is to design new compounds in silico using first-principles calculations, and then perform experiments on the computationally designed candidates. In this paper, we provide a review of ab initio computational materials design, focusing on instances in which a computational approach has been successfully applied to propose new materials of technological interest in the laboratory. Our examples include applications in renewable energy, electronic, magnetic and multiferroic materials, and catalysis, demonstrating that computationally guided materials design is a broadly applicable technique. We then discuss some of the common features and limitations of successful theoretical predictions across fields, examining the different ways in which first-principles calculations can guide the final experimental result. Finally, we present a future outlook in which we expect that new models of computational search, such as high-throughput studies, will play a greater role in guiding materials advancements
Prediction of topological phases in metastable ferromagnetic MPX monolayers
Density functional theory calculations are carried out to study the
electronic and topological properties of P ( = Mn, Fe, Co, Ni, and
= S, Se) monolayers in the ferromagnetic (FM) metastable magnetic state. We
find that FM MnPSe monolayers host topological semimetal signatures that
are gapped out when spin-orbit coupling (SOC) is included. These findings are
supported by explicit calculations of the Berry curvature and the Chern number.
The choice of the Hubbard- parameter to describe the -electrons is
thoroughly discussed, as well as the influence of using a hybrid-functional
approach. The presence of band inversions and the associated topological
features are found to be formalism-dependent. Nevertheless, routes to achieve
the topological phase via the application of external biaxial strain are
demonstrated. Within the hybrid-functional picture, topological band structures
are recovered under a pressure of 15% (17 GPa). The present work provides a
potential avenue for uncovering new topological phases in metastable
ferromagnetic phases
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