Ruddlesden-Popper Oxyfluorides La2Ni1-xCuxO3F2 (0 ≤ x ≤ 1): Impact of the Ni/Cu Ratio on the Structure

Ruddlesden-Popper oxyfluorides La2Ni1-xCuxO3F2 (0 ≤ x ≤ 1) were obtained by topochemical reaction of oxide precursors La2Ni1-xCuxO4, prepared by citrate-based soft chemistry synthesis, with polyvinylidene fluoride (PVDF) as the fluorine source. Systematic changes of the crystal structure in the oxide as well as the oxyfluoride substitution series were investigated. For 0.2 ≤ x ≤ 0.9, the oxyfluorides adopt the monoclinic (C2/c) structural distortion previously solved for the x = 0.8 compound based on neutron powder diffraction data, whereas the sample with a lower Cu content of x = 0.1 crystallizes in the orthorhombic (Cccm) structure variant of La2NiO3F2. The orthorhombic-to-monoclinic structural transition was found to be the result of an additional tilt component of the Jahn-Teller elongated CuO4F2 octahedra. The structural transitions were additionally studied by DFT calculations, confirming the monoclinic space group symmetry. The "channel-like" anionic ordering of the endmembers La2NiO3F2 and La2CuO3F2 was checked by 19F MAS NMR experiments and was found to persist throughout the entire substitution series.

Sampling the Materials Space for Conventional Superconducting Compounds

A large scale study of conventional superconducting materials using a machine-learning accelerated high-throughput workflow is performed, starting by creating a comprehensive dataset of around 7000 electron-phonon calculations performed with reasonable convergence parameters. This dataset is then used to train a robust machine learning model capable of predicting the electron-phonon and superconducting properties based on structural, compositional, and electronic ground-state properties. Using this machine, the transition temperatures (Tc ) of approximately 200 000 metallic compounds are evaluated, all of which are on the convex hull of thermodynamic stability (or close to it) to maximize the probability of synthesizability. Compounds predicted to have Tc values exceeding 5 K are further validated using density-functional perturbation theory. As a result, 541 compounds with Tc values surpassing 10 K, encompassing a variety of crystal structures and chemical compositions, are identified. This work is complemented with a detailed examination of several interesting materials, including nitrides, hydrides, and intermetallic compounds. Particularly noteworthy is LiMoN2 , which is predicted to be superconducting in the stoichiometric trigonal phase, with a Tc exceeding 38 K. LiMoN2 has previously been synthesized in this phase, further heightening its potential for practical applications.

Two-Dimensional Noble Metal Chalcogenides in the Frustrated Snub-Square Lattice

We study two-dimensional noble metal chalcogenides, with compositions {Cu, Ag, Au}2{S, Se, Te}, crystallizing in a snub-square lattice. This is a semiregular two-dimensional tesselation formed by triangles and squares that exhibits geometrical frustration. We use for comparison a square lattice, from which the snub-square tiling can be derived by a simple rotation of the squares. The monolayer snub-square chalcogenides are very close to thermodynamic stability, with the most stable system (Ag2Se) a mere 7 meV/atom above the convex hull of stability. All compounds studied in the square and snub-square lattice are semiconductors, with band gaps ranging from 0.1 to more than 2.5 eV. Excitonic effects are strong, with an exciton binding energy of around 0.3 eV. We propose the Cu (001) surface as a possible substrate to synthesize Cu2Se, although many other metal and semiconducting surfaces can be found with very good lattice matching.

Reproducibility of density functional approximations: How new functionals should be reported

Density functional theory is the workhorse of chemistry and materials science, and novel density functional approximations are published every year. To become available in program packages, the novel density functional approximations (DFAs) need to be (re)implemented. However, according to our experience as developers of Libxc [Lehtola et al., SoftwareX 7, 1 (2018)], a constant problem in this task is verification due to the lack of reliable reference data. As we discuss in this work, this lack has led to several non-equivalent implementations of functionals such as Becke-Perdew 1986, Perdew-Wang 1991, Perdew-Burke-Ernzerhof, and Becke's three-parameter hybrid functional with Lee-Yang-Parr correlation across various program packages, yielding different total energies. Through careful verification, we have also found many issues with incorrect functional forms in recent DFAs. The goal of this work is to ensure the reproducibility of DFAs. DFAs must be verifiable in order to prevent the reappearance of the above-mentioned errors and incompatibilities. A common framework for verification and testing is, therefore, needed. We suggest several ways in which reference energies can be produced with free and open source software, either with non-self-consistent calculations with tabulated atomic densities or via self-consistent calculations with various program packages. The employed numerical parameters-especially the quadrature grid-need to be converged to guarantee a ≲0.1 μEh precision in the total energy, which is nowadays routinely achievable in fully numerical calculations. Moreover, as such sub-μEh level agreement can only be achieved when fully equivalent implementations of the DFA are used, the source code of the reference implementation should also be made available in any publication describing a new DFA.

Machine-Learning-Assisted Determination of the Global Zero-Temperature Phase Diagram of Materials

Crystal-graph attention neural networks have emerged recently as remarkable tools for the prediction of thermodynamic stability. The efficacy of their learning capabilities and their reliability is however subject to the quantity and quality of the data they are fed. Previous networks exhibit strong biases due to the inhomogeneity of the training data. Here a high-quality dataset is engineered to provide a better balance across chemical and crystal-symmetry space. Crystal-graph neural networks trained with this dataset show unprecedented generalization accuracy. Such networks are applied to perform machine-learning-assisted high-throughput searches of stable materials, spanning 1 billion candidates. In this way, the number of vertices of the global T = 0 K phase diagram is increased by 30% and find more than ≈150 000 compounds with a distance to the convex hull of stability of less than 50 meV atom^-1 . The discovered materials are then accessed for applications, identifying compounds with extreme values of a few properties, such as superconductivity, superhardness, and giant gap-deformation potentials.

Many recent density functionals are numerically ill-behaved

Most computational studies in chemistry and materials science are based on the use of density functional theory. Although the exact density functional is unknown, several density functional approximations (DFAs) offer a good balance of affordable computational cost and semi-quantitative accuracy for applications. The development of DFAs still continues on many fronts, and several new DFAs aiming for improved accuracy are published every year. However, the numerical behavior of these DFAs is an often overlooked problem. In this work, we look at all 592 DFAs for three dimensional systems available in Libxc 5.2.2 and examine the convergence of the density functional total energy based on tabulated atomic Hartree-Fock wave functions. We show that several recent DFAs, including the celebrated SCAN family of functionals, show impractically slow convergence with typically used numerical quadrature schemes, making these functionals unsuitable both for routine applications or high-precision studies, as thousands of radial quadrature points may be required to achieve sub-μEh accurate total energies for these functionals, while standard quadrature grids like the SG-3 grid only contain O(100) radial quadrature points. These results are both a warning to users to always check the sufficiency of the quadrature grid when adopting novel functionals, as well as a guideline to the theory community to develop better behaved density functionals.

Efficient and improved prediction of the band offsets at semiconductorheterojunctions from meta-GGA density functionals: a benchmark study

Accurate theoretical prediction of the band offsets at interfaces of semiconductor heterostructures can of-ten be quite challenging. Although density functional theory has been reasonably successful to carry outsuch calculations and efficient and accurate semilocal functionals are desirable to reduce the computational cost. In general, the semilocal functionals based on the generalized gradient approximation (GGA) significantly underestimate the bulk band gaps. This, in turn, results in inaccurate estimates of the band offsets at the heterointerfaces. In this paper, we investigate the performance of several advanced meta-GGA functionals in the computational prediction of band offsets at semiconductor heterojunctions. In particular, we investigate the performance of r 2 SCAN (revised strongly-constrained and appropriately-normed functional), rMGGAC (revised semilocal functional based on cuspless hydrogen model and Pauli kinetic energy density functional), mTASK (modified Aschebrock and Kümmel meta-GGA functional), and LMBJ (local modified Becke-Johnson) exchange-correlation functionals. Our results strongly suggest that these meta-GGA functionals for supercell calculations perform quite well, especially, when compared to computationally more demanding GW calculations. We also present band offsets calculated using ionization potentials and electron affinities, as well as band alignment via the branch point energies. Overall, our study shows that the aforementioned meta-GGA functionals can be used within the DFT framework to estimate the band offsets in semiconductor heterostructures with predictive accuracy.

A dataset of 175k stable and metastable materials calculated with the PBEsol and SCAN functionals

In the past decade we have witnessed the appearance of large databases of calculated material properties. These are most often obtained with the Perdew-Burke-Ernzerhof (PBE) functional of density-functional theory, a well established and reliable technique that is by now the standard in materials science. However, there have been recent theoretical developments that allow for increased accuracy in the calculations. Here, we present a dataset of calculations for 175k crystalline materials obtained with two functionals: geometry optimizations are performed with PBE for solids (PBEsol) that yields consistently better geometries than the PBE functional, and energies are obtained from PBEsol and from SCAN single-point calculations at the PBEsol geometry. Our results provide an accurate overview of the landscape of stable (and nearly stable) materials, and as such can be used for reliable predictions of novel compounds. They can also be used for training machine learning models, or even for the comparison and benchmark of PBE, PBEsol, and SCAN.

Measurement(s)	optimized geometry (PBESol) • total energy (PBESol, Scan) • bandgap (PBESol, Scan)
Technology Type(s)	Density functional theory (VASP)
Factor Type(s)	Exchange correlation functional • Crystal structure

Machine learning the derivative discontinuity of density-functional theory

Machine learning is a powerful tool to design accurate, highly non-local, exchange-correlation functionals for density functional theory. So far, most of those machine learned functionals are trained for systems with an integer number of particles. As such, they are unable to reproduce some crucial and fundamental aspects, such as the explicit dependency of the functionals on the particle number or the infamous derivative discontinuity at integer particle numbers. Here we propose a solution to these problems by training a neural network as the universal functional of density-functional theory that (a) depends explicitly on the number of particles with a piece-wise linearity between the integer numbers and (b) reproduces the derivative discontinuity of the exchange-correlation energy. This is achieved by using an ensemble formalism, a training set containing fractional densities, and an explicitly discontinuous formulation.

Crystal graph attention networks for the prediction of stable materials

Graph neural networks for crystal structures typically use the atomic positions and the atomic species as input. Unfortunately, this information is not available when predicting new materials, for which the precise geometrical information is unknown. We circumvent this problem by replacing the precise bond distances with embeddings of graph distances. This allows our networks to be applied directly in high-throughput studies based on both composition and crystal structure prototype without using relaxed structures as input. To train these networks, we curate a dataset of over 2 million density functional calculations of crystals with consistent calculation parameters. We apply the resulting model to the high-throughput search of 15 million tetragonal perovskites of composition ABCD₂. As a result, we identify several thousand potentially stable compounds and demonstrate that transfer learning from the newly curated dataset reduces the required training data by 50%.

Bandgap of two-dimensional materials: Thorough assessment of modern exchange-correlation functionals

The density-functional theory (DFT) approximations that are the most accurate for the calculation of bandgap of bulk materials are hybrid functionals, such as HSE06, the modified Becke-Johnson (MBJ) potential, and the GLLB-SC potential. More recently, generalized gradient approximations (GGAs), such as HLE16, or meta-GGAs, such as (m)TASK, have also proven to be quite accurate for the bandgap. Here, the focus is on two-dimensional (2D) materials and the goal is to provide a broad overview of the performance of DFT functionals by considering a large test set of 298 2D systems. The present work is an extension of our recent studies [T. Rauch, M. A. L. Marques, and S. Botti, Phys. Rev. B 101, 245163 (2020); Patra et al., J. Phys. Chem. C 125, 11206 (2021)]. Due to the lack of experimental results for the bandgap of 2D systems, G₀W₀ results were taken as reference. It is shown that the GLLB-SC potential and mTASK functional provide the bandgaps that are the closest to G₀W₀. Following closely, the local MBJ potential has a pretty good accuracy that is similar to the accuracy of the more expensive hybrid functional HSE06.

Structure, Magnetism, and Thermal Stability of La2NiO2.5F3: A Ruddlesden-Popper Oxyfluoride Crystallizing in Space Group P42/nnm

We report on the new Ruddlesden-Popper (RP) oxyfluoride La₂NiO_2.5F₃ containing an unprecedented high amount of fluorine and Ni²⁺. This oxyfluoride was prepared by topochemical low-temperature fluorination of La₂NiO₄, which was obtained by a soft chemistry synthesis, with poly(vinylidene difluoride) (PVDF) as fluorinating agent. La₂NiO_2.5F₃ is the first n = 1 RP compound crystallizing in the tetragonal space group P4₂/nnm (a = 5.7297(6) Å and c = 13.0106(2) Å). The crystal structure shows a unique tilting scheme of the NiO₄F₂ octahedra that has so far been only theoretically predicted. Combined neutron and X-ray powder diffraction experiments together with bond-valence-sum and DFT+U calculations reveal an unusual anion ordering with fluoride being located on the apical anion sites of the NiO₄F₂ octahedra. Excess fluorine ions were found to populate two of the four interstitial anion sites in an ordered fashion. A third interstitial anion position is occupied by oxygen ions while the fourth site remains unoccupied. This hitherto unobserved ordering scenario in RP oxyfluorides promotes a strong layerwise alternating tilting of the NiO₄F₂ octahedra. Magnetic measurements show strong antiferromagnetic interactions with a high Néel temperature of about 225 K and a pronounced ZFC/FC splitting most likely as the result of a small ferromagnetic moment arising from spin canting. The electronic structure was characterized by DFT and UV-vis spectroscopy, and a strong increase of E_g was found compared to La₂NiO₄ (3.4 eV vs 1.3 eV).

Bishop's hat silicene: a planar square silicon bilayer decorated with adatoms

We investigate a family of free-standing quasi-two-dimensional silicon structures based on a planar square bilayer with adatom decorations. When attached to the bilayer, these adatoms form local reconstructions which resemble either a bishop's hat or elongated square bipyramids. We systematically constructed members of this family via exhaustive enumeration and then studied them using tight-binding and density-functional theory. We find that this geometry contributes significantly to the stability of the resulting structures, with some squared bilayers energetically more stable than the honeycomb bilayers. The most interesting phases were then characterized in more detail, and they all turned out metallic. Finally, we propose the [100] surface of ZrO2 as the most suitable substrate for the synthesis of these two-dimensional phases.

Electronic Structure of Molecules, Surfaces, and Molecules on Surfaces with the Local Modified Becke-Johnson Exchange-Correlation Potential

The knowledge of electronic properties of matter is the key to the understanding of its properties and to propose useful applications. To model hybrid organic/inorganic systems with the plane-wave approach, large supercells with many atoms are usually necessary to minimize artificial interactions between periodic images. For such systems, accurate approximations to the exchange-correlation functional of density functional theory, such as hybrid functionals, become computationally expensive, and cheaper approaches need to be considered. Here, we apply the local modified Becke-Johnson exchange-correlation potential to free molecules and surfaces and study its accuracy for calculated ionization potentials. This quantity being important to understand the band alignment of composite heterogeneous systems, we demonstrate the application of the potential to the electronic structure calculation of an exemplary composite semiconductor/molecule system, namely, a F6-TCNNQ molecule adsorbed on a hydrogenated Si(111) surface.

Atomically Thin Pythagorean Tilings in Two Dimensions

We perform a theoretical study of an atomically thin, two-dimensional layer obtained by positioning atoms at the vertices of the classical Pythagorean tiling. This leads to an unusual geometrical pattern that is only stable for the three halogens Cl, Br, and I. In this Pythagorean structure, halogen atoms are arranged in strongly bound diatomic units that bind together by weaker electrostatic bonds. The energy of these phases is competitive with those of the low-temperature phase of the halogens and the two-dimensional layer obtained by exfoliating it. The Pythagorean layers are semiconducting, with an unusual band structure composed of very mobile holes and extremely heavy electrons. They are also soft, exhibiting small values of the elastic constants and a very low energy flexural mode. Analysis of the allowed Raman transitions reveals breathing-like modes that might be used to fingerprint, experimentally, the Pythagorean structure. Finally, we present a series of substrates that, due to lattice matching and compatible symmetry, can be used to stabilize these peculiar two-dimensional layers.

First-Principles Identification of Single Photon Emitters Based on Carbon Clusters in Hexagonal Boron Nitride

A recent study associates carbon with single photon emitters (SPEs) in hexagonal boron nitride (h-BN). This observation, together with the high mobility of carbon in h-BN, suggests the existence of SPEs based on carbon clusters. Here, by means of density functional theory calculations, we studied clusters of substitutional carbon atoms up to tetramers in h-BN. Two different conformations of neutral carbon trimers have zero-point line energies and shifts of the phonon sideband compatible with typical photoluminescence spectra. Moreover, some conformations of two small C clusters next to each other result in photoluminescence spectra similar to those found in the experiments. We also showed that vacancies are unable to reproduce the typical features of the phonon sideband observed in most measurements because of the large spectral weight of low-energy breathing modes, ubiquitous in such defects.

Halogen molecular modifications at high pressure: the case of iodine

Metallization and dissociation are key transformations in diatomic molecules at high densities particularly significant for modeling giant planets. Using X-ray absorption spectroscopy and atomistic modeling, we demonstrate that in halogens, the formation of a connected molecular structure takes place at pressures well below metallization. Here we show that the iodine diatomic molecule first elongates by ∼0.007 Å up to a critical pressure of Pc ∼ 7 GPa, developing bonds between molecules. Then its length continuously decreases with pressure up to 15-20 GPa. Universal trends in halogens are shown and allow us to predict for chlorine a pressure of 42 ± 8 GPa for molecular bond-length reversal. Our findings contribute to tackling the molecule invariability paradigm in diatomic molecular phases at high pressures and may be generalized to other abundant diatomic molecules in the universe, including hydrogen.

Meta-Local Density Functionals: A New Rung on Jacob's Ladder

The homogeneous electron gas (HEG) is a key ingredient in the construction of most exchange-correlation functionals of density-functional theory. Often, the energy of the HEG is parameterized as a function of its spin density n_σ, leading to the local density approximation (LDA) for inhomogeneous systems. However, the connection between the electron density and kinetic energy density of the HEG can be used to generalize the LDA by evaluating it on a geometric average n_σ^avg(r) = n_σ^1-x(r)ñ_σ^x(r) of the local spin density n_σ(r) and the spin density ñ_σ(r) of a HEG that has the local kinetic energy density τ_σ(r) of the inhomogeneous system. This leads to a new family of functionals that we term meta-local density approximations (meta-LDAs), which are still exact for the HEG, which are derived only from properties of the HEG and which form a new rung of Jacob's ladder of density functionals [ AIP Conf. Proc. 2001, 577, 1]. The first functional of this ladder, the local τ approximation (LTA) of Ernzerhof and Scuseria [ J. Chem. Phys. 1999, 111, 911] that corresponds to x = 1 is unfortunately not stable enough to be used in self-consistent field calculations because it leads to divergent potentials, as we show in this work. However, a geometric averaging of the LDA and LTA densities with smaller values of x not only leads to numerical stability of the resulting functional but also yields more accurate exchange energies in atomic calculations than the LDA, the LTA, or the tLDA functional (x = 1/4) of Eich and Hellgren [ J. Chem. Phys. 2014, 141, 224107]. We choose x = 0.50, as it gives the best total energy in self-consistent exchange-only calculations for the argon atom. Atomization energy benchmarks confirm that the choice x = 0.50 also yields improved energetics in combination with correlation functionals in molecules, almost eliminating the well-known overbinding of the LDA and reducing its error by two thirds.

Direct insight into the structure-property relation of interfaces from constrained crystal structure prediction

A major issue that prevents a full understanding of heterogeneous materials is the lack of systematic first-principles methods to consistently predict energetics and electronic properties of reconstructed interfaces. In this work we address this problem with an efficient and accurate computational scheme. We extend the minima-hopping method implementing constraints crafted for two-dimensional atomic relaxation and enabling variations of the atomic density close to the interface. A combination of density-functional and accurate density-functional tight-binding calculations supply energy and forces to structure prediction. We demonstrate the power of this method by applying it to extract structure-property relations for a large and varied family of symmetric and asymmetric tilt boundaries in polycrystalline silicon. We find a rich polymorphism in the interface reconstructions, with recurring bonding patterns that we classify in increasing energetic order. Finally, a clear relation between bonding patterns and electrically active grain boundary states is unveiled and discussed.

The CECAM electronic structure library and the modular software development paradigm

First-principles electronic structure calculations are now accessible to a very large community of users across many disciplines, thanks to many successful software packages, some of which are described in this special issue. The traditional coding paradigm for such packages is monolithic, i.e., regardless of how modular its internal structure may be, the code is built independently from others, essentially from the compiler up, possibly with the exception of linear-algebra and message-passing libraries. This model has endured and been quite successful for decades. The successful evolution of the electronic structure methodology itself, however, has resulted in an increasing complexity and an ever longer list of features expected within all software packages, which implies a growing amount of replication between different packages, not only in the initial coding but, more importantly, every time a code needs to be re-engineered to adapt to the evolution of computer hardware architecture. The Electronic Structure Library (ESL) was initiated by CECAM (the European Centre for Atomic and Molecular Calculations) to catalyze a paradigm shift away from the monolithic model and promote modularization, with the ambition to extract common tasks from electronic structure codes and redesign them as open-source libraries available to everybody. Such libraries include "heavy-duty" ones that have the potential for a high degree of parallelization and adaptation to novel hardware within them, thereby separating the sophisticated computer science aspects of performance optimization and re-engineering from the computational science done by, e.g., physicists and chemists when implementing new ideas. We envisage that this modular paradigm will improve overall coding efficiency and enable specialists (whether they be computer scientists or computational scientists) to use their skills more effectively and will lead to a more dynamic evolution of software in the community as well as lower barriers to entry for new developers. The model comes with new challenges, though. The building and compilation of a code based on many interdependent libraries (and their versions) is a much more complex task than that of a code delivered in a single self-contained package. Here, we describe the state of the ESL, the different libraries it now contains, the short- and mid-term plans for further libraries, and the way the new challenges are faced. The ESL is a community initiative into which several pre-existing codes and their developers have contributed with their software and efforts, from which several codes are already benefiting, and which remains open to the community.

Validation of Pseudopotential Calculations for the Electronic Band Gap of Solids

Nowadays pseudopotential (PP) density functional theory calculations constitute the standard approach to tackle solid-state electronic problems. These rely on distributed PP tables that were built from all-electron atomic calculations using few popular semilocal exchange-correlation functionals, while PPs based on more modern functionals, such as meta-generalized gradient approximation and hybrid functionals, or for many-body methods, such as GW, are often not available. Because of this, employing PPs created with inconsistent exchange-correlation functionals has become a common practice. Our aim is to quantify systematically the error in the determination of the electronic band gap when cross-functional PP calculations are performed. To this end, we compare band gaps obtained with norm-conserving PPs or the projector-augmented wave method with all-electron calculations for a large data set of 473 solids. We focus, in particular, on density functionals that were designed specifically for band gap calculations. On average, the absolute error is about 0.1 eV, yielding absolute relative errors in the 5-10% range. Considering that typical errors stemming from the choice of the functional are usually larger, we conclude that the effect of choosing an inconsistent PP is rather harmless for most applications. However, we find specific cases where absolute errors can be larger than 1 eV or others where relative errors can amount to a large fraction of the band gap.

Reduced Density Matrix Functional Theory for Bosons

Based on a generalization of Hohenberg-Kohn's theorem, we propose a ground state theory for bosonic quantum systems. Since it involves the one-particle reduced density matrix γ as a variable but still recovers quantum correlations in an exact way it is particularly well suited for the accurate description of Bose-Einstein condensates. As a proof of principle we study the building block of optical lattices. The solution of the underlying v-representability problem is found and its peculiar form identifies the constrained search formalism as the ideal starting point for constructing accurate functional approximations: The exact functionals F[γ] for this N-boson Hubbard dimer and general Bogoliubov-approximated systems are determined. For Bose-Einstein condensates with N_{BEC}≈N condensed bosons, the respective gradient forces are found to diverge, ∇_{γ}F∝1/sqrt[1-N_{BEC}/N], providing a comprehensive explanation for the absence of complete condensation in nature.

Octopus, a computational framework for exploring light-driven phenomena and quantum dynamics in extended and finite systems

Over the last few years, extraordinary advances in experimental and theoretical tools have allowed us to monitor and control matter at short time and atomic scales with a high degree of precision. An appealing and challenging route toward engineering materials with tailored properties is to find ways to design or selectively manipulate materials, especially at the quantum level. To this end, having a state-of-the-art ab initio computer simulation tool that enables a reliable and accurate simulation of light-induced changes in the physical and chemical properties of complex systems is of utmost importance. The first principles real-space-based Octopus project was born with that idea in mind, i.e., to provide a unique framework that allows us to describe non-equilibrium phenomena in molecular complexes, low dimensional materials, and extended systems by accounting for electronic, ionic, and photon quantum mechanical effects within a generalized time-dependent density functional theory. This article aims to present the new features that have been implemented over the last few years, including technical developments related to performance and massive parallelism. We also describe the major theoretical developments to address ultrafast light-driven processes, such as the new theoretical framework of quantum electrodynamics density-functional formalism for the description of novel light-matter hybrid states. Those advances, and others being released soon as part of the Octopus package, will allow the scientific community to simulate and characterize spatial and time-resolved spectroscopies, ultrafast phenomena in molecules and materials, and new emergent states of matter (quantum electrodynamical-materials).

Local Modified Becke-Johnson Exchange-Correlation Potential for Interfaces, Surfaces, and Two-Dimensional Materials

The modified Becke-Johnson meta-GGA potential of density functional theory has been shown to be the best exchange-correlation potential to determine band gaps of crystalline solids. However, it cannot be consistently used for the electronic structure of nonperiodic or nanostructured systems. We propose an extension of this potential that enables its use to study heterogeneous, finite, and low-dimensional systems. This is achieved by using a coordinate-dependent expression for the parameter c that weights the Becke-Russel exchange, in contrast to the original global formulation, where c is just a fitted number. Our potential takes advantage of the excellent description of band gaps provided by the modified Becke-Johnson potential and preserves its modest computational effort. Furthermore, it yields with one single calculation band diagrams and band offsets of heterostructures and surfaces. We exemplify the usefulness and efficiency of our local meta-GGA potential by testing it for a series of interfaces (Si/SiO₂, AlAs/GaAs, AlP/GaP, and GaP/Si), a Si surface, and boron nitride monolayer.

Novel two-dimensional silicon-carbon binaries by crystal structure prediction

The semimetallic bandstructure of graphene and silicene limit their use in functional devices. Mixing silicon and carbon offers a rather unexplored pathway to build semiconducting sheets compatible with current Si-based electronics. We present here a complete theoretical study of the phase diagram of two-dimensional silicon-carbon binaries. To scan the composition range, we employ an ab initio global structural prediction method, complemented by exhaustive enumeration of two-dimensional structure prototypes. We find a wealth of two-dimensional low-energy structures, from standard honeycomb single- and double-layers, passing by dumbbell geometries, to carbon nanosheets bridged by Si atoms. Many of these phases depart from planarity, either through buckling, or by germinating three-dimensional networks with a mixture of sp2 and sp3 bonds. We further characterize the most interesting crystal structures, unveiling a large variety of electronic properties, that could be exploited to develop high-performance electronic devices at the nanoscale.

Machine Learning the Physical Nonlocal Exchange-Correlation Functional of Density-Functional Theory

We train a neural network as the universal exchange-correlation functional of density-functional theory that simultaneously reproduces both the exact exchange-correlation energy and the potential. This functional is extremely nonlocal but retains the computational scaling of traditional local or semilocal approximations. It therefore holds the promise of solving some of the delocalization problems that plague density-functional theory, while maintaining the computational efficiency that characterizes the Kohn-Sham equations. Furthermore, by using automatic differentiation, a capability present in modern machine-learning frameworks, we impose the exact mathematical relation between the exchange-correlation energy and the potential, leading to a fully consistent method. We demonstrate the feasibility of our approach by looking at one-dimensional systems with two strongly correlated electrons, where density-functional methods are known to fail, and investigate the behavior and performance of our functional by varying the degree of nonlocality.

Large-Scale Benchmark of Exchange-Correlation Functionals for the Determination of Electronic Band Gaps of Solids

We compile a large data set designed for the efficient benchmarking of exchange–correlation functionals for the calculation of electronic band gaps. The data set comprises information on the experimental structure and band gap of 472 nonmagnetic materials and includes a diverse group of covalent-, ionic-, and van der Waals-bonded solids. We used it to benchmark 12 functionals, ranging from standard local and semilocal functionals, passing through meta-generalized-gradient approximations, and several hybrids. We included both general purpose functionals, like the Perdew–Burke–Ernzerhof approximation, and functionals specifically crafted for the determination of band gaps. The comparison of experimental and theoretical band gaps shows that the modified Becke–Johnson is at the moment the best available density functional, closely followed by the Heyd–Scuseria–Ernzerhof screened hybrid from 2006 and the high-local-exchange generalized-gradient approximation.

Computational acceleration of prospective dopant discovery in cuprous iodide

The zinc blende (γ) phase of copper iodide holds the record hole conductivity for intrinsic transparent p-type semiconductors. In this work, we employ a high-throughput approach to systematically explore strategies for enhancing γ-CuI further by impurity incorporation. Our objectives are not only to find a practical approach to increase the hole conductivity in CuI thin films, but also to explore the possibility for ambivalent doping. In total 64 chemical elements were investigated as possible substitutionals on either the copper or the iodine site. All chalcogen elements were found to display acceptor character when substituting iodine, with sulfur and selenium significantly enhancing carrier concentrations produced by the native V_Cu defects under conditions most favorable for impurity incorporation. Furthermore, eight impurities suitable for n-type doping were discovered. Unfortunately, our work also reveals that donor doping is hindered by compensating native defects, making ambipolar doping unlikely. Finally, we investigated how the presence of impurities influences the optical properties. In the majority of the interesting cases, we found no deep states in the band-gap, showing that CuI remains transparent upon doping.

Neural network force fields for simple metals and semiconductors: construction and application to the calculation of phonons and melting temperatures

We present a practical procedure to obtain reliable and unbiased neural network based force fields for solids. Training and test sets are efficiently generated from global structural prediction runs, at the same time assuring the structural variety and importance of sampling the relevant regions of phase space. The neural networks are trained to yield not only good formation energies, but also accurate forces and stresses, which are the quantities of interest for molecular dynamics simulations. Finally, we construct, as an example, several force fields for both semiconducting and metallic elements, and prove their accuracy for a variety of structural and dynamical properties. These are then used to study the melting of bulk copper and gold.

Propagators for the Time-Dependent Kohn-Sham Equations: Multistep, Runge-Kutta, Exponential Runge-Kutta, and Commutator Free Magnus Methods

We examine various integration schemes for the time-dependent Kohn-Sham equations. Contrary to the time-dependent Schrödinger's equation, this set of equations is nonlinear, due to the dependence of the Hamiltonian on the electronic density. We discuss some of their exact properties, and in particular their symplectic structure. Four different families of propagators are considered, specifically the linear multistep, Runge-Kutta, exponential Runge-Kutta, and the commutator-free Magnus schemes. These have been chosen because they have been largely ignored in the past for time-dependent electronic structure calculations. The performance is analyzed in terms of cost-versus-accuracy. The clear winner, in terms of robustness, simplicity, and efficiency is a simplified version of a fourth-order commutator-free Magnus integrator. However, in some specific cases, other propagators, such as some implicit versions of the multistep methods, may be useful.

Towards a formal definition of static and dynamic electronic correlations

Some of the most spectacular failures of density-functional and Hartree-Fock theories are related to an incorrect description of the so-called static electron correlation. Motivated by recent progress in the N-representability problem of the one-body density matrix for pure states, we propose a method to quantify the static contribution to the electronic correlation. By studying several molecular systems we show that our proposal correlates well with our intuition of static and dynamic electron correlation. Our results bring out the paramount importance of the occupancy of the highest occupied natural spin-orbital in such quantification.

Emergence of superconductivity in doped H2O ice at high pressure

We investigate the possibility of achieving high-temperature superconductivity in hydrides under pressure by inducing metallization of otherwise insulating phases through doping, a path previously used to render standard semiconductors superconducting at ambient pressure. Following this idea, we study H2O, one of the most abundant and well-studied substances, we identify nitrogen as the most likely and promising substitution/dopant. We show that for realistic levels of doping of a few percent, the phase X of ice becomes superconducting with a critical temperature of about 60 K at 150 GPa. In view of the vast number of hydrides that are strongly covalent bonded, but that remain insulating up to rather large pressures, our results open a series of new possibilities in the quest for novel high-temperature superconductors.

High-throughput search of ternary chalcogenides for p-type transparent electrodes

Delafossite crystals are fascinating ternary oxides that have demonstrated transparent conductivity and ambipolar doping. Here we use a high-throughput approach based on density functional theory to find delafossite and related layered phases of composition ABX₂, where A and B are elements of the periodic table, and X is a chalcogen (O, S, Se, and Te). From the 15 624 compounds studied in the trigonal delafossite prototype structure, 285 are within 50 meV/atom from the convex hull of stability. These compounds are further investigated using global structural prediction methods to obtain their lowest-energy crystal structure. We find 79 systems not present in the materials project database that are thermodynamically stable and crystallize in the delafossite or in closely related structures. These novel phases are then characterized by calculating their band gaps and hole effective masses. This characterization unveils a large diversity of properties, ranging from normal metals, magnetic metals, and some candidate compounds for p-type transparent electrodes.

Insights into colour-tuning of chlorophyll optical response in green plants

First-principles calculations within the framework of real-space time-dependent density functional theory have been performed for the complete chlorophyll (Chl) network of the light-harvesting complex from green plants, LHC-II. A local-dipole analysis method developed for this work has made possible the studies of the optical response of individual Chl molecules subjected to the influence of the remainder of the chromophore network. The spectra calculated using our real-space TDDFT method agree with previous suggestions that weak interaction with the protein microenvironment should produce only minor changes in the absorption spectrum of Chl chromophores in LHC-II. In addition, relative shifting of Chl absorption energies leads the stromal and lumenal sides of LHC-II to absorb in slightly different parts of the visible spectrum providing greater coverage of the available light frequencies. The site-specific alterations in Chl excitation energies support the existence of intrinsic energy transfer pathways within the LHC-II complex.

Real-space grids and the Octopus code as tools for the development of new simulation approaches for electronic systems

Real-space grids are a powerful alternative for the simulation of electronic systems. One of the main advantages of the approach is the flexibility and simplicity of working directly in real space where the different fields are discretized on a grid, combined with competitive numerical performance and great potential for parallelization. These properties constitute a great advantage at the time of implementing and testing new physical models. Based on our experience with the Octopus code, in this article we discuss how the real-space approach has allowed for the recent development of new ideas for the simulation of electronic systems. Among these applications are approaches to calculate response properties, modeling of photoemission, optimal control of quantum systems, simulation of plasmonic systems, and the exact solution of the Schrödinger equation for low-dimensionality systems.

Topological Crystalline Insulator in a New Bi Semiconducting Phase

Topological crystalline insulators are a type of topological insulators whose topological surface states are protected by a crystal symmetry, thus the surface gap can be tuned by applying strain or an electric field. In this paper we predict by means of ab initio calculations a new phase of Bi which is a topological crystalline insulator characterized by a mirror Chern number n_M = −2, but not a strong topological insulator. This system presents an exceptional property: at the (001) surface its Dirac cones are pinned at the surface high-symmetry points. As a consequence they are also protected by time-reversal symmetry and can survive against weak disorder even if in-plane mirror symmetry is broken at the surface. Taking advantage of this dual protection, we present a strategy to tune the band-gap based on a topological phase transition unique to this system. Since the spin-texture of these topological surface states reduces the back-scattering in carrier transport, this effective band-engineering is expected to be suitable for electronic and optoelectronic devices with reduced dissipation.

Investigation of new phases in the Ba–Si phase diagram under high pressure using ab initio structural search

Barium silicides are versatile materials that have attracted attention for a variety of applications in electronics and optoelectronics.

Nanostructured water and carbon dioxide inside collapsing carbon nanotubes at high pressure

We present simulations of the collapse under hydrostatic pressure of carbon nanotubes containing either water or carbon dioxide.