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 Fe$_2$YZ 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 MPX3_3 monolayers

Density functional theory calculations are carried out to study the electronic and topological properties of $M$P$X_3$ ($M$ = Mn, Fe, Co, Ni, and $X$ = S, Se) monolayers in the ferromagnetic (FM) metastable magnetic state. We find that FM MnPSe$_3$ 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-$U$ parameter to describe the $d$-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