arXiv subjects

Rickard Armiento

Publications and source records attributed to Rickard Armiento.

35 records · Page 2Linked to original sources

arXiv2023

The Chlorine Vacancy in 4H-SiC: An NV-like Defect With Telecom Emission

The diamond nitrogen vacancy (NV) center remains an ever increasing topic of interest. At present, it is considered an ideal example of a solid-state qubit applicable in quantum communication, computing, and sensing alike. With its success, the search for defects that share or improve upon its advantageous features is an ongoing endeavor. By performing large-scale high-throughput screening of 52600 defects in 4H silicon carbide (SiC), we identify a collection of NV-like color-centers of particular interest. From this list, the single most promising candidate consists of a silicon vacancy and chlorine substituted on the carbon site, and is given the name of the chlorine vacancy (ClV) center. Through high-accuracy first-principle calculations, we confirm that the ClV center is similar to the NV center in diamond in its local structure and shares many qualitative and quantitative features in the electronic structure and spin properties. In contrast to the NV center, however, the ClV center in SiC exhibits emission in the telecom range near the C-band.

cond-mat.mtrl-sci↗

arXiv2023

Absorption $\textit{versus}$ Adsorption: High-Throughput Computation of Impurities in 2D Materials

Doping of a two-dimensional (2D) material by impurity atoms occurs \textit{via} two distinct mechanisms: absorption of the dopants by the 2D crystal or adsorption on its surface. To distinguish the relevant mechanism, we systematically dope 53 experimentally synthesized 2D monolayers by 65 different chemical elements in both absorption and adsorption sites. The resulting 17,598 doped monolayer structures were generated using the newly developed ASE \texttt{DefectBuilder} -- a Python tool to set up point defects in 2D and bulk materials -- and subsequently relaxed by an automated high-throughput density functional theory (DFT) workflow. We find that interstitial positions are preferred for small dopants with partially filled valence electrons in host materials with large lattice parameters. On the contrary, adatoms are favored for dopants with a low number of valence electrons due to lower coordination of adsorption sites compared to interstitials. The relaxed structures, characterization parameters, defect formation energies, and magnetic moments (spins) are available in an open database to help advance our understanding of defects in 2D materials.

cond-mat.mtrl-sci↗

arXiv2022

Exhaustive characterization of modified Si vacancies in 4H-SiC

The negatively charged silicon vacancy ($\mathrm{V_{Si}^-}$) in silicon carbide is a well-studied point defect for quantum applications. At the same time, a closer inspection of ensemble photoluminescence and electron paramagnetic resonance measurements reveals an abundance of related but so far unidentified signals. In this study, we search for defects in 4H-SiC that explain the above magneto-optical signals in a defect database generated by Automatic Defect Analysis and Qualification (ADAQ) workflows. This search reveals only one class of atomic structures that exhibit silicon-vacancy-like properties in the data: a carbon antisite ($\mathrm{C_{Si}}$) within sub-nanometer distances from the silicon vacancy only slightly alters the latter without affecting the charge or spin state. Such a perturbation is energetically bound. We consider the formation of $\mathrm{V_{Si}^-+C_{Si}}$ up to 2 nm distance and report their zero phonon lines and zero field splitting values. In addition, we perform high-resolution photoluminescence experiments in the silicon vacancy region and find an abundance of lines. Comparing our computational and experimental results, several configurations show great agreement. Our work demonstrates the effectiveness of a database with high-throughput results in the search for defects in quantum applications.

cond-mat.mtrl-sci↗

arXiv2022

Rapid Discovery of Stable Materials by Coordinate-free Coarse Graining

A fundamental challenge in materials science pertains to elucidating the relationship between stoichiometry, stability, structure, and property. Recent advances have shown that machine learning can be used to learn such relationships, allowing the stability and functional properties of materials to be accurately predicted. However, most of these approaches use atomic coordinates as input and are thus bottle-necked by crystal structure identification when investigating novel materials. Our approach solves this bottleneck by coarse-graining the infinite search space of atomic coordinates into a combinatorially enumerable search space. The key idea is to use Wyckoff representations -- coordinate-free sets of symmetry-related positions in a crystal -- as the input to a machine learning model. Our model demonstrates exceptionally high precision in discovering new theoretically stable materials, identifying 1,569 materials that lie below the known convex hull of previously calculated materials from just 5,675 ab-initio calculations. Our approach opens up fundamental advances in computational materials discovery.

cond-mat.mtrl-sci↗

arXiv2021

OPTIMADE, an API for exchanging materials data

The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification.

cond-mat.mtrl-sci↗

arXiv2021

ADAQ: Automatic workflows for magneto-optical properties of point defects in semiconductors

Automatic Defect Analysis and Qualification (ADAQ) is a collection of automatic workflows developed for high-throughput simulations of magneto-optical properties of point defect in semiconductors. These workflows handle the vast number of defects by automating the processes to relax the unit cell of the host material, construct supercells, create point defect clusters, and execute calculations in both the electronic ground and excited states. The main outputs are the magneto-optical properties which include zero-phonon lines, zero-field splitting, and hyperfine coupling parameters. In addition, the formation energies are calculated. We demonstrate the capability of ADAQ by performing a complete characterization of the silicon vacancy in silicon carbide in the polytype 4H (4H-SiC).

cond-mat.mtrl-sci↗

arXiv2021

Graph-based machine learning beyond stable materials and relaxed crystal structures

There has been a recent surge of interest in using machine learning to approximate density functional theory (DFT) in materials science. However, many of the most performant models are evaluated on large databases of computed properties of, primarily, materials with precise atomic coordinates available, and which have been experimentally synthesized, i.e., which are thermodynamically stable or metastable. These aspects provide challenges when applying such models on theoretical candidate materials, for example for materials discovery, where the coordinates are not known. To extend the scope of this methodology, we investigate the performance of the Crystal Graph Convolutional Neural Network (CGCNN) on a data set of theoretical structures in three related ternary phase diagrams (Ti,Zr,Hf)-Zn-N, which thus include many highly unstable structures. We then investigate the impact on the performance of using atomic positions that are only partially relaxed into local energy minima. We also explore options for improving the performance in these scenarios by transfer learning, either from models trained on a large database of mostly stable systems, or a different but related phase diagram. Models pre-trained on stable materials do not significantly improve performance, but models trained on similar data transfer very well. We demonstrate how our findings can be utilized to generate phase diagrams with a major reduction in computational effort.

cond-mat.mtrl-sci↗

arXiv2021

Identification of materials with strong magneto-structural coupling using computational high-throughput screening

Important phenomena such as magnetostriction, magnetocaloric, and magnetoelectric effects arise from, or could be enhanced by, the coupling of magnetic and structural degrees of freedom. The coupling of spin and lattice also influence transport and structural properties in magnetic materials in particular around phase transitions. In this paper we propose a method for screening materials for a strong magneto-structural coupling by assessing the effect of the local magnetic configuration on the atomic forces using density functional theory (DFT). We have employed the disordered local moment approach in a supercell formulation to probe different magnetic local configurations and their forces and performed a high-throughput search on binary and ternary compounds available in the Crystallographic Open Database. We identify a list of materials with a strong spin-lattice coupling out of which several are already known to display magneto-lattice coupling-phenomena like Fe3O4 and CrN. Others, such as Mn2CrO4 and CaFe7O11 have been less studied and are yet to reveal their potentials in experiments and applications.

cond-mat.mtrl-sci↗

arXiv2020

An Ontology for the Materials Design Domain

In the materials design domain, much of the data from materials calculations are stored in different heterogeneous databases. Materials databases usually have different data models. Therefore, the users have to face the challenges to find the data from adequate sources and integrate data from multiple sources. Ontologies and ontology-based techniques can address such problems as the formal representation of domain knowledge can make data more available and interoperable among different systems. In this paper, we introduce the Materials Design Ontology (MDO), which defines concepts and relations to cover knowledge in the field of materials design. MDO is designed using domain knowledge in materials science (especially in solid-state physics), and is guided by the data from several databases in the materials design field. We show the application of the MDO to materials data retrieved from well-known materials databases.

cs.DB↗

arXiv2019

Database-driven High-Throughput Calculations and Machine Learning Models for Materials Design

This paper reviews past and ongoing efforts in using high-throughput ab-inito calculations in combination with machine learning models for materials design. The primary focus is on bulk materials, i.e., materials with fixed, ordered, crystal structures, although the methods naturally extend into more complicated configurations. Efficient and robust computational methods, computational power, and reliable methods for automated database-driven high-throughput computation are combined to produce high-quality data sets. This data can be used to train machine learning models for predicting the stability of bulk materials and their properties. The underlying computational methods and the tools for automated calculations are discussed in some detail. Various machine learning models and, in particular, descriptors for general use in materials design are also covered.

cond-mat.mtrl-sci↗

arXiv2019

Semi-Local Parameterization of the Electron Localization Function in Second-Order Density Gradients

The electron localization function (ELF) is a universal measure of electron localization that allows for, e.g., an effective characterization of physical bonds in molecular and solid state systems. In the context of the widely used Kohn-Sham density-functional theory (KS-DFT) and its generalizations, ELF is given in terms of the single-particle electron density as well as the non-interacting kinetic energy density (KED) of the KS system. Starting from the notion of an edge electron gas put forth by Kohn and Mattsson, we here use an \emph{exactly soluble}, strongly correlated few-electron model of a harmonically confined electron gas in order to parameterize the positive-definite non-interacting KS KED in terms of the density and its reduced second-order gradients. We arrive at a simple, yet generally applicable functional approximation to ELF expressed in the electron density and its derivatives. To demonstrate the validity of our approach, we use the obtained parameterization to perform topological analysis of the bonds in solid Al and Si, and to study physical and chemical adsorption of graphene on a Ni surface. We find that while the expression does not provide a quantitatively accurate approximation of absolute ELF values, the most essential qualitative features are captured. Hence, the expression is useful in contexts where the electron density is available, but not the KS orbitals or the KED, and one desires a qualitative picture of the electron localization that mimics ELF.

cond-mat.str-el↗

arXiv2017

First principles predictions of magneto-optical data for semiconductor defects: the case of divacancy defects in 4H-SiC

Study and design of magneto-optically active single point defects in semiconductors are rapidly growing fields due to their potential in quantum bit and single photon emitter applications. Detailed understanding of the properties of candidate defects is essential for these applications, and requires the identification of the defects microscopic configuration and electronic structure. Multi-component semiconductors often host two or more non-equivalent configurations of point defects. These configurations generally exhibit similar electronic structure and basic functionalities, however, they differ in details that are of great importance whenever single defect applications are considered. Identification of non-equivalent configurations of point defects is thus essential for successful single defect manipulation and application. A promising way to identify defects is via comparison of experimental measurements and results of first-principle calculations. We investigate a possibility to produce accurate ab initio data for zero-phonon lines and hyperfine coupling parameters that are required for systematic quantum bit search. We focus on properties relevant for the possible use of the divacancy defect in quantum bits in 4H-SiC. We provide a decisive identification of divacancy configurations in 4H-SiC and clarify differences in prior predictions of 4H-SiC divacancy zero-phonon photoluminescence lines.

cond-mat.mtrl-sci↗

arXiv2016

Machine Learning Energies of 2 M Elpasolite (ABC$_2$D$_6$) Crystals

Elpasolite is the predominant quaternary crystal structure (AlNaK$_2$F$_6$ prototype) reported in the Inorganic Crystal Structure Database. We have developed a machine learning model to calculate density functional theory quality formation energies of all $\sim$2 M pristine ABC$_2$D$_6$ elpasolite crystals which can be made up from main-group elements (up to bismuth). Our model's accuracy can be improved systematically, reaching 0.1 eV/atom for a training set consisting of 10 k crystals. Important bonding trends are revealed, fluoride is best suited to fit the coordination of the D site which lowers the formation energy whereas the opposite is found for carbon. The bonding contribution of elements A and B is very small on average. Low formation energies result from A and B being late elements from group (II), C being a late (I) element, and D being fluoride. Out of 2 M crystals, 90 unique structures are predicted to be on the convex hull---among which NFAl$_2$Ca$_6$, with peculiar stoichiometry and a negative atomic oxidation state for Al.

physics.chem-ph↗

arXiv2016

Structural and electronic properties of Li intercalated graphene on SiC(0001)

We investigate the structural and electronic properties of Li-intercalated monolayer graphene on SiC(0001) using combined angle-resolved photoemission spectroscopy and first-principles density functional theory. Li intercalates at room temperature both at the interface between the buffer layer and SiC and between the two carbon layers. The graphene is strongly $n$-doped due to charge transfer from the Li atoms and two $π$-bands are visible at the $\bar{K}$-point. After heating the sample to 300$^\circ$C, these $π$-bands become sharp and have a distinctly different dispersion to that of Bernal-stacked bilayer graphene. We suggest that the Li atoms intercalate between the two carbon layers with an ordered structure, similar to that of bulk LiC$_6$. An AA-stacking of these two layers becomes energetically favourable. The $π$-bands around the $\bar{K}$-point closely resemble the calculated band structure of a C$_6$LiC$_6$ system, where the intercalated Li atoms impose a super-potential on the graphene electronic structure that opens pseudo-gaps at the Dirac points of the two $π$-cones.

cond-mat.mtrl-sci↗

arXiv2015

Crystal Structure Representations for Machine Learning Models of Formation Energies

We introduce and evaluate a set of feature vector representations of crystal structures for machine learning (ML) models of formation energies of solids. ML models of atomization energies of organic molecules have been successful using a Coulomb matrix representation of the molecule. We consider three ways to generalize such representations to periodic systems: (i) a matrix where each element is related to the Ewald sum of the electrostatic interaction between two different atoms in the unit cell repeated over the lattice; (ii) an extended Coulomb-like matrix that takes into account a number of neighboring unit cells; and (iii) an Ansatz that mimics the periodicity and the basic features of the elements in the Ewald sum matrix by using a sine function of the crystal coordinates of the atoms. The representations are compared for a Laplacian kernel with Manhattan norm, trained to reproduce formation energies using a data set of 3938 crystal structures obtained from the Materials Project. For training sets consisting of 3000 crystals, the generalization error in predicting formation energies of new structures corresponds to (i) 0.49, (ii) 0.64, and (iii) 0.37 eV/atom for the respective representations.

physics.chem-ph↗

arXiv2014

Theoretical unification of hybrid-DFT and DFT+U methods for the treatment of localized orbitals

We formulate the on-site occupation dependent exchange correlation energy and effective potential of hybrid functionals for localized states and connect them to the on-site correction term of the DFT+U method. Our derivation provides a theoretical justification for adding a DFT+U-like onsite potential in hybrid DFT calculations to resolve issues caused by overscreening of localized states. The resulting scheme, hybrid- DFT+Vw, is tested for chromium impurity in wurtzite AlN and vanadium impurity in 4H-SiC, which are paradigm examples of systems with different degree of localization between host and impurity orbitals.

cond-mat.str-el↗

arXiv2013

High-Throughput Screening of Perovskite Alloys for Piezoelectric Performance and Formability

We screen a large chemical space of perovskite alloys for systems with the right properties to accommodate a morphotropic phase boundary (MPB) in their composition-temperature phase diagram, a crucial feature for high piezoelectric performance. We start from alloy end-points previously identified in a high-throughput computational search. An interpolation scheme is used to estimate the relative energies between different perovskite distortions for alloy compositions with a minimum of computational effort. Suggested alloys are further screened for thermodynamic stability. The screening identifies alloy systems already known to host a MPB, and suggests a few new ones that may be promising candidates for future experiments. Our method of investigation may be extended to other perovskite systems, e.g., (oxy-)nitrides, and provides a useful methodology for any application of high-throughput screening of isovalent alloy systems.

cond-mat.mtrl-sci↗