We present the implementation of spinor quantum transport within the non-equilibrium Green's function (NEGF) code TranSIESTA based on Density Functional Theory (DFT). First-principles methods play an essential role in molecular and material modelling, and the DFT+NEGF approach has become a widely-used tool for quantum transport simulation. Existing (open-source) DFT-based quantum transport codes either model nonequilibrium/finite-bias cases in an approximate way or rely on the collinear spin approximation. Our new implementation closes this gap and enables the TranSIESTA code to use full spinor-wave functions. Thereby it provides a method for transport simulation of topological materials and devices based on spin-orbit coupling (SOC) or non-collinear spins. These materials hold enormous potential for the development of ultra-low-energy electronics urgently needed for the design of sustainable technology. The new feature is tested on relevant systems determining magnetoresistance in iron nanostructures and transport properties of a lateral transition metal dichalcogenide heterojunction.

We introduce a combined density functional theory (DFT) and non-equilibrium Green's function (NEGF) framework to compute the capacitance of nanocapacitors and directly extract the dielectric response of a sub-nanometer dielectric under bias. We identify that at the nanoscale conventional capacitance evaluations based on stored charge per unit voltage suffer from an ill-posed partitioning of electrode and dielectric charge. This partitioning directly impacts the geometric definition of capacitance through the capacitor width, which in turn makes the evaluation of dielectric response uncertain. This ambiguous separation further induces spurious interfacial polarizability when analyzed via maximally localized Wannier functions. Focusing on crystalline ice, we develop a robust charge-separation protocol that yields unique capacitance-derived polarizability and dielectric constants, unequivocally demonstrating that confinement neither alters ice's intrinsic electronic response nor its insensitivity to proton order. Our results lay the groundwork for rigorous interpretation of capacitor measurements in low-dimensional dielectric materials.

The electron density is a fundamental observable of an atomic system from which all ground-state properties can be computed. As a prediction target for machine learning (ML) models, electron density is often represented on a dense real space grid, which is data heavy, or through density fitting approximations. In this work, we show the power of targeting the density matrix (DM), a linear-scaling sparse SE(3) equivariant matrix that encodes the exact density. We introduce Graph2Mat, a universal function for converting molecular graphs into equivariant matrices. We demonstrate how a ML model that combines this Graph2Mat approach with state-of-the-art molecular graph representations can accurately predict the DM of molecular systems. The models achieve state-of-the-art performance on electron density prediction by matching the accuracy of grid-based methods, while using datasets that are at least one order of magnitude smaller. Accurately predicted electron densities can also accelerate density functional theory (DFT) calculations by reducing the number of self-consistent field (SCF) iterations needed to converge. In this work, we get an average 40% reduction on the number of SCF steps in DFT calculations of QM9 molecules with SIESTA. The novel prediction model also allows for two new and promising measures of uncertainty (total charge error and self-consistency error) that will facilitate its practical usage, e.g. within active learning workflows. These results open the door for many applications using hybrid ML-accelerated DFT methodologies, and uncertainty aware single iteration ab initio molecular dynamics.

This perspective addresses the topic of harnessing the tools of artificial intelligence (AI) for boosting innovation in functional materials design and engineering as well as discovering new materials for targeted applications in energy storage, biomedicine, composites, nanoelectronics or quantum technologies. It gives a current view of experts in the field, insisting on challenges and opportunities provided by the development of large materials databases, novel schemes for implementing AI into materials production and characterization as well as progress in the quest of simulating physical and chemical properties of realistic atomic models reaching the trillion atoms scale and with near ab initio accuracy.

(Scanning) transmission electron microscopy ((S)TEM) has significantly advanced materials science but faces challenges in correlating precise atomic structure information with the functional properties of devices due to its time-intensive nature. To address this, an analytical workflow is introduced for the holistic characterization, modelling, and simulation of device heterostructures. This workflow automates the experimental (S)TEM data analysis, providing an in-depth characterization of crystallographic information, 3D orientation, elemental composition, and strain distribution. It reduces a process that typically takes days for a trained human into an automatic routine solved in minutes. Utilizing a physics-guided artificial intelligence model, it generates representative descriptions of materials and samples. The workflow culminates in creating digital twins of systems limited with at least one axis of translational invariance -3D finite element and atomic models of millions of atoms-enabling simulations that provide crucial insights into device behavior in practical applications. Demonstrated with SiGe planar heterostructures for scalable spin qubits, the workflow links digital twins to theoretical properties, revealing how atomic structure impacts materials and functional properties such as spatially-resolved phononic or electronic characteristics, or (inverse) spin orbit lengths. The versatility of the workflow is demonstrated through its application to a wide array of materials systems, device configurations, and sample morphologies.

We present SIESTA-BdG, an implementation of the simultaneous solution of the Bogoliubov-de Gennes (BdG) and Density Functional Theory (DFT) problem in SIESTA, a first-principles method and code for material simulations which uses pseudopotentials and a localized basis set. This unified approach describes both conventional and unconventional superconducting states, and enables a description of inhomogeneous superconductors and heterostructures. We demonstrate the validity, accuracy, and efficiency of SIESTA-BdG by computing physically relevant quantities (superconducting charge density, band structure, superconducting gap features, density of states) for conventional singlet (Nb, Pb) and unconventional (FeSe) superconductors. We find excellent agreement with experiments and results obtained within the KKR-BdG computational framework. SIESTA-BdG forms the basis for modelling quantum transport in superconducting devices and including - in an approximate fashion - the superconducting DFT potential of Oliveira, Gross, and Kohn.

This article introduces a groundbreaking analytical workflow designed for the holistic characterisation, modelling and physical simulation of device heterostructures. Our innovative workflow autonomously, comprehensively and locally characterises the crystallographic information and 3D orientation of the crystal phases, the elemental composition, and the strain maps of devices from (scanning) transmission electron microscopy data. It converts a manual characterisation process that traditionally takes days into an automatic routine completed in minutes. This is achieved through a physics-guided artificial intelligence model that combines unsupervised and supervised machine learning in a modular way to provide a representative 3D description of the devices, materials structures, or samples under analysis. To culminate the process, we integrate the extracted knowledge to automate the generation of both 3D finite element and atomic models of millions of atoms acting as digital twins, enabling simulations that yield essential physical and chemical insights crucial for understanding the device's behaviour in practical applications. We prove this end-to-end workflow with a state-of-the-art materials platform based on SiGe planar heterostructures for hosting coherent and scalable spin qubits. Our workflow connects representative digital twins of the experimental devices with their theoretical properties to reveal the true impact that every atom in the structure has on their electronic properties, and eventually, into their functional quantum performance. Notably, the versatility of our workflow is demonstrated through its successful application to a wide array of materials systems, device configurations and sample morphologies.

In the last few years, organic corrosion inhibitors have been used as a green alternative to toxic inorganic compounds to prevent corrosion in materials. Nonetheless, the fundamental mechanisms determining their inhibition performance are still far from understood. Molecular modeling can provide important insights into those mechanisms, allowing for a detailed analysis of the corrosion inhibition (CI) process. However, CI modeling is frequently underexplored and commonly used in a standardized way following a pre-determined recipe to support experimental data. We highlight six fundamental aspects (A) that one should consider when modeling CI: (A1) the electronic properties of isolated inhibitors, (A2) the interaction of the inhibitor with the surface, (A3) the surface model, (A4) the effect of the anodic and cathodic zones on the surface, (A5) the solvent effects, and (A6) the electrodes’ potential effects. While A1-A3 are more frequently investigated, A4-A6 and some more complex surface models from A3 are usually not considered and represent gaps in the CI modeling literature. In this review, we discuss the main features of molecular modeling applied to CI, considering the aforementioned key aspects and focusing on the gaps that the emerging approaches aim to fill. Filling these gaps will allow performing more detailed simulations of the CI process, which, coupled with artificial intelligence (AI) methods and multiscale approaches, might construct the bridge between the nanoscale CI modeling and the continuum scale of the CI processes.

The origin of the charge density wave (CDW) instabilities in the isostructural but not isoelectronic octahedral layers of the three-dimensional solids gamma-Mo 4 O 11 and P 4 W 12 O 44 is discussed on the basis of first-principles density functional theory calculations. These layers contain three different and superposed one-dimensional (1D) systems (two diagonal and one horizontal) associated with the three t 2 g orbitals of the transition metal in octahedral coordination. Because of the special topology of the layers the three 1D systems are practically independent (hidden nesting) and the Lindhard function contains three different lines of intensity maxima associated with each of them. Clear cusps (six for P 4 W 12 O 44 and four for gamma-Mo 4 O 11 ) occur at the intersections of these intensity lines. The wave vector of the structural modulations associated with some of these cusps from our calculations is in good agreement with the observed CDW wave vectors. The nature of the different modulations is analyzed on the basis of the calculated thermal dependence of intrachain and interchain coherence lengths of the diffuse lines associated with the diagonal and horizontal chains. Modulation in the diagonal chains is found to be more favorable than in the horizontal chain. The same type of wave vector is selected for gamma-Mo 4 O 11 and P 4 W 12 O 44 despite having a different band filling. The coupling of the electronic instability to the phonon spectra and the relationship between the nature of the high-temperature modulation with the width of the octahedral layers is discussed. Among the two Magn & eacute;li phases the interlayer coupling is found to be somewhat stronger in eta-Mo 4 O 11 . The relationship with other hidden-nesting series of materials as the rare-earth tellurides is commented.

Transition metal dichalcogenides (TMDs) are a class of layered materials that hold great promise for a wide range of applications. Their practical use can be limited by their thermal transport properties, which have proven challenging to determine accurately, both from a theoretical and experimental perspective. We have conducted a thorough theoretical investigation of the thermal conductivity of four common TMDs, MoSe 2 , WSe 2 , MoS 2 , and WS 2 , at room temperature, to determine the key factors that influence their thermal behavior. We analyze these materials using ab initio calculations performed with the SIESTA program, anharmonic lattice dynamics and the Boltzmann transport equation formalism, as implemented in the temperature -dependent effective potentials method. Within this framework, we analyze the microscopic parameters influencing the thermal conductivity, such as the phonon dispersion and the phonon lifetimes. The aim is to precisely identify the origin of differences in thermal conductivity among these canonical TMD materials. We compare their in -plane thermal properties in monolayer and bulk form, and we analyze how the thickness and the chemical composition affect the thermal transport behavior. We showcase how bonding and the crystal structure influence the thermal properties by comparing the TMDs with silicon, reporting the cases of bulk silicon and monolayer silicene. We find that the interlayer bond type (covalent vs. van der Waals) involved in the structure is crucial in the heat transport. In two-dimensional silicene, we observe a reduction by a factor similar to 15 compared to the Si bulk thermal conductivity due to the smaller group velocities and shorter phonon lifetimes. In the TMDs, where the group velocities and the phonon bands do not vary significantly passing from the bulk to the monolayer limit, we do not see as strong a decrease in the thermal conductivity: only a factor 2-3. Moreover, our analysis reveals that differences in the thermal conductivity arise from variations in atomic species, bond strengths, and phonon lifetimes. These factors are closely interconnected and collectively impact the overall thermal conductivity. We inspect each of them separately and explain how they influence the heat transport. We also study artificial TMDs with modified masses, in order to assess how the chemistry of the compounds modifies the microscopic quantities and thus the thermal conductivity.

Spin-electronic devices are poised to become part of mainstream microelectronic technology .Downsizing them, however, faces the intrinsic difficulty that as ferromagnets become smaller, it becomes more difficult to stabilize their magnetic moment. Antiferromagnets are much more stable, and thus research on antiferromagnetic spintronics has developed into a fast-growing field. Here, we provide proof of concept data that allows us to expand the area of antiferromagnetic spintronics to the hitherto elusive level of individual molecules. In contrast to all previous work on molecular spintronics, our detection scheme of the molecule's spin state does not rely on a magnetic moment. Instead, we use field-effect transistor devices constituting of an isolated, contacted single-wall carbon nanotube covalently bound to a limited number of molecular antiferromagnets incorporating four Mn(II) or Co(II) ions. Time-dependent quantum transport measurement along the functionalized nanotube show step-like transitions between several distinct current levels, which we attribute to transitions between different antiferromagnetic states of individual molecular complexes grafted on the nanotube. A statistical analysis of the switching events using factorial cumulants indicates that the cobalt complexes switch independently from each other, while a coherent superposition of the antiferromagnetic spin states of the molecules along the nanotube is observed for the manganese complexes. The long coherence time (several seconds at 100 mK) is made possible by the absence of spin and orbital momentum in the relevant states of the manganese complex, while the cobalt complex includes a significant orbital momentum contribution due to the pseudo-octahedral d$^7$ metal centers.

We present a code modularization approach to design efficient and massively parallel cubic- and linear-scaling solvers for electronic structure calculations using atomic orbitals. The modular implementation of the orbital minimization method, in which linear algebra and parallelization issues are handled via external libraries, is demonstrated in the SIESTA code. The distributed block compressed sparse row (DBCSR) and scalable linear algebra package (ScaLAPACK) libraries are used for algebraic operations with sparse and dense matrices, respectively. The MatrixSwitch and libOMM libraries, recently developed within the Electronic Structure Library, facilitate switching between different matrix formats and implement the energy minimization. We show results comparing the performance of several cubic-scaling algorithms, and also demonstrate the parallel performance of the linear-scaling solvers, and their supremacy over the cubic-scaling solvers for insulating systems with sizes of several hundreds of atoms.

Diffusion is one of the most ubiquitous transport phenomena in nature. Experimentally, it can be tracked by following point spreading in space and time. Here, we introduce a spatiotemporal pump-probe microscopy technique that exploits the residual spatial temperature profile obtained through the transient reflectivity when probe pulses arrive before pump pulses. This corresponds to an effective pump-probe time delay of 13 ns, determined by the repetition rate of our laser system (76 MHz). This pre-time-zero technique enables probing the diffusion of long-lived excitations created by previous pump pulses with nanometer accuracy and is particularly powerful for following in-plane heat diffusion in thin films. The particular advantage of this technique is that it enables quantifying thermal transport without requiring any material input parameters or strong heating. We demonstrate the direct determination of the thermal diffusivities of films with a thickness of around 15 nm, consisting of the layered materials MoSe2 (0.18 cm2/s), WSe2 (0.20 cm2/s), MoS2 (0.35 cm2/s), and WS2 (0.59 cm2/s). This technique paves the way for observing nanoscale thermal transport phenomena and tracking diffusion of a broad range of species.

The choice of the ideal material employed in selector devices is a tough task both from the theoretical and experimental side, especially due to the lack of a synergistic approach between techniques able to correlate specific material properties with device characteristics. Using a material-to-device multiscale technique, a reliable protocol for an efficient characterization of the active traps in amorphous GeSe chalcogenide is proposed. The resulting trap maps trace back the specific features of materials responsible for the measured findings, and connect them to an atomistic description of the sample. The metrological approach can be straightforwardly extended to other materials and devices, which is very beneficial for an efficient material-device codesign and the optimization of novel technologies.

Nanometer scale lateral heterostructures with atomically sharp band discontinuities can be conceived as the 2D analogues of vertical Van der Waals heterostructures, where pristine properties of each component coexist with interfacial phenomena that result in a variety of exotic quantum phenomena. However, despite considerable advances in the fabrication of lateral heterostructures, controlling their covalent interfaces and band discontinuities with atomic precision, scaling down components and producing periodic, lattice-coherent superlattices still represent major challenges. Here, a synthetic strategy to fabricate nanometer scale, coherent lateral superlattice heterojunctions with atomically sharp band discontinuity is reported. By merging interdigitated arrays of different types of graphene nanoribbons by means of a novel on-surface reaction, superlattices of 1D, and chemically heterogeneous nanoporous junctions are obtained. The latter host subnanometer quantum dipoles and tunneling in-gap states, altogether expected to promote interfacial phenomena such as interribbon excitons or selective photocatalysis.

Tailoring magnetic properties of antiferromagnetic coordination clusters by changing the inner core.

Proximity effects are one of the pillars of exotic phenomena and technological applications of two dimensional materials. However, the interactions nature depends strongly on the materials involved, their crystalline symmetries, and interfacial properties. Here we used large-scale first-principle calculations to demonstrate that strain and twist-angle are efficient knobs to tailor the spin-orbit coupling in graphene transition metal dichalcogenide heterobilayers. We found that by choosing a twist-angle of 30 degrees, the spin relaxation times increase by two orders of magnitude, opening a path to improve these heterostructures spin transport capability. Moreover, we demonstrate that strain and twist angle will modify the relative values of valley-Zeeman and Rashba spin-orbit coupling, allowing to tune the system into an ideal Dirac-Rashba regime. These results enable us to envision an answer for the variability of spin-orbit coupling found in different experiments and have significant consequences for applications that depend on polycrystallinity, where grains form at different orientations.

Diamond is one of the most studied materials because of its unique combination of remarkable electrical, mechanical, thermal and optical properties. Using a fully self-consistent ab initio theory of coupled electron-phonon transport, we reveal another striking behavior: a huge drag enhancement of the thermopower of lightly doped diamond. Thermopower values of around 100,000 microvolts per Kelvin are found at 100 K, significantly exceeding the highest previously measured value in the correlated metal FeSb2, and occurring at much higher temperatures. The enormous thermopower in diamond arises primarily from exceptionally weak anharmonic phonon decay around and below 100 K that facilitates efficient momentum exchange between charge carriers and phonons through electron-phonon interactions. Exceedingly large thermoelectric power factors are also identified. This work gives insights into the physics of the coupled electron-phonon system in solids and advances our understanding of thermoelectric transport in the regime of strong drag.

The origin of the charge density wave in TaTe4 is discussed on the basis of a first-principles density functional theory analysis of the Fermi surface, electron-hole response function, phonon band structure of the average structure, and structural optimization of the modulated phase. Analysis of the band structure and Fermi surface of the average structure clearly proves that despite the presence of TaTe4 chains in the crystal structure, TaTe4 is in fact a 3D material as far as the electronic structure near the Fermi level is concerned. A Fermi surface nesting mechanism is dismissed as the origin of the 2a x 2a x 3c structural modulation. The optimized 2a x 2a x 3c structure, which is found to be the more stable modulation in agreement with the experimental observations, can be obtained directly from a soft-phonon mode computed for the undistorted structure. Our results suggest that the driving force for the distortion is the maximization of Ta-Ta metal-metal bonding subject to inducing the minimum bonding decrease in the Te sublattice.