arXiv subjects

Christian Carbogno

Publications and source records attributed to Christian Carbogno.

At least 19 recordsLinked to original sources

arXiv2026

Twin Domains in Van der Waals Quaternary Oxides

Optical anisotropy is the basis for many intriguing phenomena in van der Waals materials, including hyperbolic polaritons and extreme birefringence. Stacking and twisting van der Waals materials along the out-of-plane direction emerged as a powerful route to tailor this anisotropy, but designing lateral interfaces remains a challenge. Here, twin domains are reported in the van der Waals quaternary oxides MgTeMoO$_6$, MnTeMoO$_6$, ZnTeMoO$_6$, and CoTeMoO$_6$ - materials that possess strong in-plane optical anisotropy and second-order nonlinearity. The domains naturally form in their orthorhombic crystal structure and extend over hundreds of micrometers. It is proposed that this stability is achieved by the domain wall acting as a diagonal mirror plane in the crystal structure, parallel to the (1-10) or (110) crystal planes, resulting in nearly opposite birefringence between domains. This hypothesis is experimentally confirmed by determining the angle between the crystal axes of neighboring domains using polarization-resolved optical microscopy, infrared-visible sum-frequency generation microscopy, and transmission electron microscopy. The latter further allowed an estimate of the domain wall thickness. Overall, the observation of twin domains with orthogonal optical anisotropy opens new routes to use van der Waals quaternary oxides for birefringent waveguiding, polariton steering, and frequency conversion applications.

cond-mat.mtrl-sci↗

arXiv2026

General-purpose LLMs as Constrained Crystal Composition Generators

The targeted discovery of inorganic materials remains challenging due to the vastness of compositional design spaces and the high cost of exhaustive screening. Task-specific generative artificial intelligence represents a particularly efficient alternative to screening, yet demands tedious collection of training data before providing real benefit. General-purpose large language models (LLMs) have recently shown tremendous potential for the targeted generation of single, optimal materials compositions without the need for task-specific fine-tuning. However, it is unclear whether LLMs generally pose an advantage compared to specialized generative models, in particular in large design spaces. Here, we demonstrate that such models are capable of covering entire regions of the targeted property space effectively and systematically. Using Elpasolite materials as an established benchmark for generative tasks in large chemical spaces, we find that an iterative prompt-and-response framework is able to recover on average 96% of all low-energy Elpasolites in the target region. This performance, driven mainly by iterative in-context learning, surpasses the generative abilities of previous, task-specific models. Our results establish general-purpose LLMs as flexible and accessible components for inverse materials design workflows.

cond-mat.mtrl-sci↗

arXiv2026

Roadmap on Advancements of the FHI-aims Software Package

Electronic-structure theory is the foundation of the description of materials including multiscale modeling of their properties and functions. Obviously, without sufficient accuracy at the base, reliable predictions are unlikely at any level that follows. The software package FHI-aims has proven to be a game changer for accurate free-energy calculations because of its scalability, numerical precision, and its efficient handling of density functional theory (DFT) with hybrid functionals and van der Waals interactions. It treats molecules, clusters, and extended systems (solids and liquids) on an equal footing. Besides DFT, FHI-aims also includes quantum-chemistry methods, descriptions for excited states and vibrations, and calculations of various types of transport. Recent advancements address the integration of FHI-aims into an increasing number of workflows and various artificial intelligence (AI) methods. This Roadmap describes the state-of-the-art of FHI-aims and advancements that are currently ongoing or planned.

cond-mat.mtrl-sci↗

arXiv2026

Efficient Band Structure Unfolding with Atom-centered Orbitals: General Theory and Application

Band structure unfolding is a key technique for analyzing and simplifying the electronic band structure of large, internally distorted supercells that break the primitive cell's translational symmetry. In this work, we present an efficient band unfolding method for atomic orbital (AO) basis sets that explicitly accounts for both the non-orthogonality of atomic orbitals and their atom-centered nature. Unlike existing approaches that typically rely on a plane-wave representation of the (semi-)valence states, we here derive analytical expressions that recasts the primitive cell translational operator and the associated Bloch-functions in the supercell AO basis. In turn, this enables the accurate and efficient unfolding of conduction, valence, and core states in all-electron codes, as demonstrated by our implementation in the all-electron ab initio simulation package FHI-aims, which employs numeric atom-centered orbitals. We explicitly demonstrate the capability of running large-scale unfolding calculations for systems with thousands of atoms and showcase the importance of this technique for computing temperature-dependent spectral functions in strongly anharmonic materials using CuI as example.

cond-mat.mtrl-sci↗

arXiv2025

Electric-Field Driven Nuclear Dynamics of Liquids and Solids from a Multi-Valued Machine-Learned Dipolar Model

The driving of vibrational motion by external electric fields is a topic of continued interest, due to the possibility of assessing new or metastable material phases with desirable properties. Here, we combine ab initio molecular dynamics within the electric-dipole approximation with machine-learning neural networks (NNs) to develop a general, efficient and accurate method to perform electric-field-driven nuclear dynamics for molecules, solids, and liquids. We train equivariant and autodifferentiable NNs for the interatomic potential and the dipole, modifying the model infrastructure to account for the multi-valued nature of the latter in periodic systems. We showcase the method by addressing property modifications induced by electric field interactions in a polar liquid and a polar solid from nanosecond-long molecular dynamics simulations with quantum-mechanical accuracy. For liquid water, we present a calculation of the dielectric function in the GHz to THz range and the electrofreezing transition, showing that nuclear quantum effects enhance this phenomenon. For the ferroelectric perovskite LiNbO$_3$, we simulate the ferroelectric to paraelectric phase transition and the non-equilibrium dynamics of driven phonon modes related to the polarization switching mechanisms, showing that a full polarization switch is not achieved in the simulations.

cond-mat.mtrl-sci↗

arXiv2025

Spectral tuning of hyperbolic shear polaritons in monoclinic gallium oxide via isotopic substitution

Hyperbolic phonon polaritons - hybridized modes arising from the ultrastrong coupling of infrared light to strongly anisotropic lattice vibrations in uniaxial or biaxial polar crystals - enable to confine light to the nanoscale with low losses and high directionality. In even lower symmetry materials, such as monoclinic $β$-Ga$_2$O$_3$ (bGO), hyperbolic shear polaritons (HShPs) further enhance the directionality. Yet, HShPs are intrinsically supported only within narrow frequency ranges defined by the phonon frequencies of the host material. Here, we report spectral tuning of HShPs in bGO by isotopic substitution. Employing near-field optical microscopy to image HShPs in $^{18}$O bGO films homo-epitaxially grown on a $^{16}$O bGO substrate, we demonstrate a spectral redshift of $\sim~40~$cm$^{-1}$ for the $^{18}$O bGO, compared to $^{16}$O bGO. The technique allows for direct observation and a model-free estimation of the spectral shift driven by isotopic substitution without the need for knowledge of the dielectric tensor. Complementary far-field measurements and ab initio calculations - in good agreement with the near-field data - confirm the effectiveness of this estimation. This multifaceted study demonstrates a significant isotopic substitution induced spectral tuning of HShPs into a previously inaccessible frequency range, creating new avenues for technological applications of such highly directional polaritons.

physics.optics↗

arXiv2025

Full Crystallographic Imaging of Hexagonal Boron Nitride Monolayers with Phonon-Enhanced Sum-Frequency Microscopy

Hexagonal boron nitride (hBN) is an important 2D material for van der Waals heterostructures, single photon emitters, and infrared nanophotonics. The optical characterization of mono- and few-layer samples of hBN however remains a challenge as the material is almost invisible optically. Here we introduce phase-resolved sum-frequency microscopy as a technique for imaging monolayers of hBN grown by chemical vapor deposition (CVD) and visualize their crystal orientation. A combination of femtosecond mid-infrared (IR) and visible laser pulses is used for sum-frequency generation (SFG), which is imaged in a wide-field optical microscope. The IR laser resonantly excites a phonon of hBN that leads to an ~800-fold enhancement of the SFG intensity, making it possible to image large 100x100 μm2 sample areas in less than 1 s. Implementing heterodyne detection in combination with azimuthal rotation of the sample further provides full crystallographic information. Through combined knowledge of topography and crystal orientation, we find that triangular domains of CVD-grown monolayer hBN have nitrogen-terminated zigzag edges. Overall, SFG microscopy can be used as an ultra-sensitive tool to image crystal structure, strain, stacking sequences, and twist angles, and is applicable to the wide range of van der Waals structures, where location and identification of monolayer regions and interfaces with broken inversion symmetry is of paramount importance.

cond-mat.mtrl-sci↗

arXiv2025

Advancing our Understanding of Optoionic Effects for the Design of Solar Batteries: A Theoretical Perspective

Optoionics, a promising new field that aims at controlling ion dynamics using light, links photovoltaic power generation with electrochemical charge storage. This has the potential to drive and accelerate the energy revolution by utilizing materials that integrate the functionality of batteriesand photovoltaic cells. Finding, optimizing, and customizing these materials is a complex task, though. Computational modeling can play a crucial role in guiding and speeding up these processes, particularly when the atomic mechanisms are not well understood. This does however require expertise in various areas, including advanced electronic-structure theory, machine learning, and multi-scale approaches. In this perspective, we shed light on the intricacies of modeling optoionic effects for solar battery materials. We first discuss the underlying physical and chemical mechanisms, as well as the computational tools that are available to date for describing these processes. Furthermore, we discuss the limits of these approaches and identify key challenges that need to be tackled to advance this field.

cond-mat.mtrl-sci↗

arXiv2025

Density-Functional Perturbation Theory with Numeric Atom-Centered Orbitals

This paper represents one contribution to a larger Roadmap article reviewing the current status of the FHI-aims code. In this contribution, the implementation of density-functional perturbation theory in a numerical atom-centered framework is summarized. Guidelines on usage and links to tutorials are provided.

cond-mat.mtrl-sci↗

arXiv2025

Polarisation, Born Effective Charges, and Topological Invariants via a Berry-Phase Approach

This paper represents one contribution to a larger Roadmap article reviewing the current status of the FHI-aims code. In this contribution, the implementation of polarization, Born-effective charges and topological invariants using a Berry-phase approach in a all-electron, numeric atom-centered orbitals framework is summarized. Guidelines on usage and links to tutorials are provided.

cond-mat.mtrl-sci↗

arXiv2024

Temperature-dependent Electronic Spectral Functions from Band-Structure Unfolding

The electronic band structure, describing the periodic dependence of electronic quantum states on lattice momentum in reciprocal space, is a fundamental concept in solid-state physics. However, it's only well-defined for static nuclei. To account for thermodynamic effects, this concept must be generalized by introducing the temperature-dependent spectral function, which characterizes the finite-width distributions of electronic quantum states at each reciprocal vector. Many-body perturbation theory can compute spectral functions and associated observables, but it approximates the dynamics of nuclei and its coupling to the electrons using the harmonic approximation and linear-order electron-phonon coupling elements, respectively. These approximations may fail at elevated temperatures or for mobile atoms. To avoid inaccuracies, the electronic spectral function can be obtained non-perturbatively, capturing higher-order couplings between electrons and vibrational degrees of freedom. This process involves recovering the representation of supercell bands in the first Brillouin zone of the primitive cell, a process known as unfolding. In this contribution, we describe the implementation of the band-structure unfolding technique in the electronic-structure theory package FHI-aims and the updates made since its original development.

cond-mat.mtrl-sci↗

arXiv2024

Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learning

Molecular dynamics (MD) employing machine-learned interatomic potentials (MLIPs) serve as an efficient, urgently needed complement to ab initio molecular dynamics (aiMD). By training these potentials on data generated from ab initio methods, their averaged predictions can exhibit comparable performance to ab initio methods at a fraction of the cost. However, insufficient training sets might lead to an improper description of the dynamics in strongly anharmonic materials, because critical effects might be overlooked in relevant cases, or only incorrectly captured, or hallucinated by the MLIP when they are not actually present. In this work, we show that an active learning scheme that combines MD with MLIPs (MLIP-MD) and uncertainty estimates can avoid such problematic predictions. In short, efficient MLIP-MD is used to explore configuration space quickly, whereby an acquisition function based on uncertainty estimates and on energetic viability is employed to maximize the value of the newly generated data and to focus on the most unfamiliar but reasonably accessible regions of phase space. To verify our methodology, we screen over 112 materials and identify 10 examples experiencing the aforementioned problems. Using CuI and AgGaSe$_2$ as archetypes for these problematic materials, we discuss the physical implications for strongly anharmonic effects and demonstrate how the developed active learning scheme can address these issues.

cond-mat.mtrl-sci↗

arXiv2024

Carrier Mobility of Strongly Anharmonic Materials from First Principles

First-principle approaches for phonon-limited electronic transport are typically based on many-body perturbation theory and transport equations. With that, they rely on the validity of the quasi-particle picture for electrons and phonons, which is known to fail in strongly anharmonic systems. In this work, we demonstrated the relevance of effects beyond the quasi-particle picture by combining ab initio molecular dynamics and the Kubo-Greenwood (KG) formalism to establish a non-perturbative, stochastic method to calculate carrier mobilities while accounting for all orders of anharmonic and electron-vibrational couplings. In particular, we propose and exploit several numerical strategies that overcome the notoriously slow convergence of the KG formalism for both electronic and nuclear degree of freedom in crystalline solids. The capability of this method is demonstrated by calculating the temperature-dependent electron mobility of the strongly anharmonic oxide perovskites SrTiO3 and BaTiO3 across a wide range of temperatures. We show that the temperature-dependence of the mobility is largely driven by anharmonic, higher-order coupling effects and rationalize these trends in terms of the non-perturbative electronic spectral functions.

cond-mat.mtrl-sci↗

arXiv2024

Roadmap on Data-Centric Materials Science

Science is and always has been based on data, but the terms "data-centric" and the "4th paradigm of" materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of Artificial Intelligence (AI) and its subset Machine Learning (ML), has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research.

cond-mat.mtrl-sci↗

arXiv2024

Efficient All-electron Hybrid Density Functionals for Atomistic Simulations Beyond 10,000 Atoms

Hybrid density functional approximations (DFAs) offer compelling accuracy for ab initio electronic-structure simulations of molecules, nanosystems, and bulk materials, addressing some deficiencies of computationally cheaper, frequently used semilocal DFAs. However, the computational bottleneck of hybrid DFAs is the evaluation of the non-local exact exchange contribution, which is the limiting factor for the application of the method for large-scale simulations. In this work, we present a drastically optimized resolution-of-identity-based real-space implementation of the exact exchange evaluation for both non-periodic and periodic boundary conditions in the all-electron code FHI-aims, targeting high-performance CPU compute clusters. The introduction of several new refined Message Passing Interface (MPI) parallelization layers and shared memory arrays according to the MPI-3 standard were the key components of the optimization. We demonstrate significant improvements of memory and performance efficiency, scalability, and workload distribution, extending the reach of hybrid DFAs to simulation sizes beyond ten thousand atoms. As a necessary byproduct of this work, other code parts in FHI-aims have been optimized as well, e.g., the computation of the Hartree potential and the evaluation of the force and stress components. We benchmark the performance and scaling of the hybrid DFA based simulations for a broad range of chemical systems, including hybrid organic-inorganic perovskites, organic crystals and ice crystals with up to 30,576 atoms (101,920 electrons described by 244,608 basis functions).

cond-mat.mtrl-sci↗

arXiv2024

Self-interaction corrected SCAN functional for molecules and solids in the numeric atom-center orbital framework

Semilocal density-functional approximations (DFAs), including the state-of-the-art SCAN functional, are plagued by the self-interaction error (SIE). While this error is explicitly defined only for one-electron systems, it has inspired the self-interaction correction method proposed by Perdew and Zunger (PZ-SIC), which has shown promise in mitigating the many-electron SIE. However, the PZ-SIC method is known for its significant numerical instability. In this study, we introduce a novel constraint that facilitates self-consistent localization of the SIC orbitals in the spirit of Edmiston-Ruedenberg orbitals [Rev. Mod. Phys. 35, 457 (1963)]. Our practical implementation within the all-electron numeric atom-centered orbitals code FHI-aims guarantees efficient and stable convergence of the self-consistent PZ-SIC equations for both molecules and solids. We further demonstrate that our PZ-SIC approach effectively mitigates the SIE in the meta-GGA SCAN functional, significantly improving the accuracy for ionization potentials, charge-transfer energies, and band gaps for a diverse selection of molecules and solids. However, our PZ-SIC method does have its limitations. It can not improve the already accurate SCAN results for properties such as cohesive energies, lattice constants, and bulk modulus in our test sets. This highlights the need for new-generation DFAs with more comprehensive applicability.

cond-mat.mtrl-sci↗

arXiv2023

Spontaneous polarization in NaNbO$_{3}$ film on NdGaO$_{3}$ and DyScO$_{3}$ substrates

Pure NaNbO$_{3}$ is an antiferroelectric material at room temperature that irreversibly transforms to a ferroelectric polar state when subjected to an external electrical field or lattice strain. Experimentally, it has been observed that NaNbO$_{3}$ films grown on NdGaO$_{3}$ exhibit an electrical polarization along the [001]$_{\mathrm{PC}}$ direction, whereas films on DyScO$_{3}$ substrates exhibit a polarization along the [011]$_{\mathrm{PC}}$ direction. These effects have been attributed to the realization of different lattice symmetries in the films due to the incorporation of lattice strain imposed by the use of oxide substrates with different lattice parameters. However, the underlying atomistic mechanisms of the resulting phase symmetry in the films are hardly clear, given that NaNbO$_{3}$ features a diverse and complex phase diagram. In turn, these also impede a straightforward tailoring and optimization of the resulting macroscopic properties on different substrates. To clarify this issue, we perform all-electron first-principles calculations for several potential NaNbO$_{3}$ polymorphs under stress and strain. The computed properties, including the ferroelectric polarization, reveal that an orthorhombic $Pmc2_{1}$ phase is realized on NdGaO$_{3}$ substrates since this is the only phase with an out-of-plane polarization under a compressive strain. Conversely, the monoclinic $Pm$ phase is consistent for the samples grown on DyScO$_{3}$ substrate, since this phase exhibits a spontaneous in-plane polarization along [011]$_{\mathrm{PC}}$ under tensile strain.

cond-mat.mtrl-sci↗

arXiv2023

On the Uncertainty Estimates of Equivariant-Neural-Network-Ensembles Interatomic Potentials

Machine-learning (ML) interatomic potentials (IPs) trained on first-principles datasets are becoming increasingly popular since they promise to treat larger system sizes and longer time scales, compared to the {\em ab initio} techniques producing the training data. Estimating the accuracy of MLIPs and reliably detecting when predictions become inaccurate is key for enabling their unfailing usage. In this paper, we explore this aspect for a specific class of MLIPs, the equivariant-neural-network (ENN) IPs using the ensemble technique for quantifying their prediction uncertainties. We critically examine the robustness of uncertainties when the ENN ensemble IP (ENNE-IP) is applied to the realistic and physically relevant scenario of predicting local-minima structures in the configurational space. The ENNE-IP is trained on data for liquid silicon, created by density-functional theory (DFT) with the generalized gradient approximation (GGA) for the exchange-correlation functional. Then, the ensemble-derived uncertainties are compared with the actual errors (comparing the results of the ENNE-IP with those of the underlying DFT-GGA theory) for various test sets, including liquid silicon at different temperatures and out-of-training-domain data such as solid phases with and without point defects as well as surfaces. Our study reveals that the predicted uncertainties are generally overconfident and hold little quantitative predictive power for the actual errors.

cond-mat.mtrl-sci↗