arXiv subjects

Nicola Marzari

Publications and source records attributed to Nicola Marzari.

At least 73 records · Page 4Linked to original sources

arXiv2025

First-principles Hubbard parameters with automated and reproducible workflows

We introduce an automated, flexible framework (aiida-hubbard) to self-consistently calculate Hubbard $U$ and $V$ parameters from first-principles. By leveraging density-functional perturbation theory, the computation of the Hubbard parameters is efficiently parallelized using multiple concurrent and inexpensive primitive cell calculations. Furthermore, the intersite $V$ parameters are defined on-the-fly during the iterative procedure to account for atomic relaxations and diverse coordination environments. We demonstrate the scalability and reliability of the framework by computing in high-throughput fashion the self-consistent onsite $U$ and intersite $V$ parameters for 115 Li-containing bulk solids. Our analysis of the Hubbard parameters calculated reveals a significant correlation of the onsite $U$ values on the oxidation state and coordination environment of the atom on which the Hubbard manifold is centered, while intersite $V$ values exhibit a general decay with increasing interatomic distance. We find, e.g., that the numerical values of $U$ for Fe and Mn 3d orbitals can vary up to 3 eV and 6 eV, respectively; their distribution is characterized by typical shifts of about 0.5 eV and 1.0 eV upon change in oxidation state, or local coordination environment. For the intersite $V$ a narrower spread is found, with values ranging between 0.2 eV and 1.6 eV when considering transition metal and oxygen interactions. This framework paves the way for the exploration of redox materials chemistry and high-throughput screening of $d$ and $f$ compounds across diverse research areas, including the discovery and design of novel energy storage materials, as well as other technologically-relevant applications.

cond-mat.mtrl-sci↗

arXiv2025

Machine learning Hubbard parameters with equivariant neural networks

Density-functional theory with extended Hubbard functionals (DFT+$U$+$V$) provides a robust framework to accurately describe complex materials containing transition-metal or rare-earth elements. It does so by mitigating self-interaction errors inherent to semi-local functionals which are particularly pronounced in systems with partially-filled d and f electronic states. However, achieving accuracy in this approach hinges upon the accurate determination of the on-site $U$ and inter-site $V$ Hubbard parameters. In practice, these are obtained either by semi-empirical tuning, requiring prior knowledge, or, more correctly, by using predictive but expensive first-principles calculations. Here, we present a machine learning model based on equivariant neural networks which uses atomic occupation matrices as descriptors, directly capturing the electronic structure, local chemical environment, and oxidation states of the system at hand. We target here the prediction of Hubbard parameters computed self-consistently with iterative linear-response calculations, as implemented in density-functional perturbation theory (DFPT), and structural relaxations. Remarkably, when trained on data from 12 materials spanning various crystal structures and compositions, our model achieves mean absolute relative errors of 3% and 5% for Hubbard $U$ and $V$ parameters, respectively. By circumventing computationally expensive DFT or DFPT self-consistent protocols, our model significantly expedites the prediction of Hubbard parameters with negligible computational overhead, while approaching the accuracy of DFPT. Moreover, owing to its robust transferability, the model facilitates accelerated materials discovery and design via high-throughput calculations, with relevance for various technological applications.

cond-mat.mtrl-sci↗

arXiv2025

The thermodynamics of CaSiO3 in Earth's lower mantle

The lower mantle of Earth, characterized by pressures of 24-127 GPa and temperatures of 1900-2600 K, is still inaccessible to direct observations. In this work, we investigate by first principles the stability, phase diagram, elastic properties, and thermal conductivity of CaSiO3, that constitutes a significant component of Earth's lower mantle. Notably, our simulations capture in full the anharmonic ionic fluctuations arising from the extreme temperatures and pressures of the lower mantle, thanks to the use of stochastic self-consistant harmonic approximation (SSCHA). We show that the cubic phase of CaSiO3 is the stable state at the lower mantle's thermodynamic conditions. The phase boundary between the cubic and tetragonal phases is of first-order and increases linearly from 300 K to 1000 K between 12 GPa and 100 GPa. Accounting for temperature-renormalized phonon dispersions, we evaluate the speed of sound as a function of depth. Our results downplay the role of octahedral rotations on the transverse sound velocity of cubic CaSiO3, advocated in the past to explain discrepancies between theory and experiments. The lattice thermal conductivity, assessed thanks to the recently introduced Wigner formalism, shows a predominance of particle-like transport, thus justifying the use of the standard Boltzmann transport equation even in a system with such strong ionic anharmonicity.

cond-mat.mtrl-sci↗

arXiv2024

Ion Sieving in Two-Dimensional Membranes from First Principles

A first-principles approach for calculating ion separation in solution through two-dimensional (2D) membranes is proposed and applied. Ionic energy profiles across the membrane are obtained first, where solvation effects are simulated explicitly with machine-learning molecular dynamics, electrostatic corrections are applied to remove finite-size capacitive effects, and a mean-field treatment of the charging of the electrochemical double layer is used. Entropic contributions are assessed analytically and validated against thermodynamic integration. Ionic separations are then inferred through a microkinetic model of the filtration process, accounting for steady-state charge separation effects across the membrane. The approach is applied to Li$^{+}$, Na$^{+}$, K$^{+}$ sieving through a crown-ether functionalized graphene membrane, with a case study of the mechanisms for a highly selective and efficient extraction of lithium from aqueous solutions.

cond-mat.soft↗

arXiv2024

The Wannier Function Software Ecosystem for Materials Simulations

Over the last two decades, following the early developments on maximally localized Wannier functions, an ecosystem of electronic-structure simulation techniques and software packages leveraging the Wannier representation has flourished. This environment includes codes to obtain Wannier functions and interfaces with first-principles simulation software, as well as an increasing number of related post-processing packages. Wannier functions can be obtained for isolated or extended systems (both crystalline and disordered), and can be used to understand chemical bonding, to characterize electric polarization, magnetization, and topology, or as an optimal basis set, providing very accurate interpolations in reciprocal space or large-scale Hamiltonians in real space. In this review, we summarize the current landscape of techniques, materials properties and simulation codes based on Wannier functions that have been made accessible to the research community, and that are now well integrated into what we term a \emph{Wannier function software ecosystem}. First, we introduce the theory and practicalities of Wannier functions, starting from their broad domains of applicability to advanced minimization methods using alternative approaches beyond maximal localization. Then we define the concept of a Wannier ecosystem and its interactions and interoperability with many quantum simulations engines and post-processing packages. We focus on some of the key properties and capabilities that are empowered by such ecosystem\textemdash from band interpolations and large-scale simulations to electronic transport, Berryology, topology, electron-phonon couplings, dynamical mean-field theory, embedding, and Koopmans functionals\textemdash concluding with the current status of interoperability and automation. [...]

cond-mat.mtrl-sci↗

arXiv2024

Electrostatic interactions in atomistic and machine-learned potentials for polar materials

Long-range electrostatic interactions critically affect polar materials. However, state-of-the-art atomistic potentials, such as neural networks or Gaussian approximation potentials employed in large-scale simulations, often neglect the role of these long-range electrostatic interactions. This study introduces a novel model derived from first principles to evaluate the contribution of long-range electrostatic interactions to total energies, forces, and stresses. The model is designed to integrate seamlessly with existing short-range force fields without further first-principles calculations or retraining. The approach relies solely on physical observables, like the dielectric tensor and Born effective charges, that can be consistently calculated from first principles. We demonstrate that the model reproduces critical features, such as the LO-TO splitting and the long-wavelength phonon dispersions of polar materials, with benchmark results on the cubic phase of barium titanate (BaTiO$_3$).

cond-mat.mtrl-sci↗

arXiv2024

Teaching oxidation states to neural networks

While the accurate description of redox reactions remains a challenge for first-principles calculations, it has been shown that extended Hubbard functionals (DFT+U+V) can provide a reliable approach, mitigating self-interaction errors, in materials with strongly localized d or f electrons. Here, we first show that DFT+U+V molecular dynamics is capable to follow the adiabatic evolution of oxidation states over time, using representative Li-ion cathode materials. In turn, this allows to develop redox-aware machine-learned potentials. We show that considering atoms with different oxidation states (as accurately predicted by DFT+U+V) as distinct species in the training leads to potentials that are able to identify the correct ground state and pattern of oxidation states for redox elements present. This can be achieved, e.g., through a systematic combinatorial search for the lowest energy configuration or with stochastic methods. This brings the advantages of machine-learned potentials to key technological applications (e.g., rechargeable batteries), which require an accurate description of the evolution of redox states.

cond-mat.mtrl-sci↗

arXiv2024

Predicting electronic screening for fast Koopmans spectral functional calculations

Koopmans spectral functionals are a powerful extension of Kohn-Sham density-functional theory (DFT) that enable the prediction of spectral properties with state-of-the-art accuracy. The success of these functionals relies on capturing the effects of electronic screening through scalar, orbital-dependent parameters. These parameters have to be computed for every calculation, making Koopmans spectral functionals more expensive than their DFT counterparts. In this work, we present a machine-learning model that -- with minimal training -- can predict these screening parameters directly from orbital densities calculated at the DFT level. We show on two prototypical use cases that using the screening parameters predicted by this model, instead of those calculated from linear response, leads to orbital energies that differ by less than 20 meV on average. Since this approach dramatically reduces run-times with minimal loss of accuracy, it will enable the application of Koopmans spectral functionals to classes of problems that previously would have been prohibitively expensive, such as the prediction of temperature-dependent spectral properties. More broadly, this work demonstrates that measuring violations of piecewise linearity (i.e. curvature in total energies with respect to occupancies) can be done efficiently by combining frozen-orbital approximations and machine learning.

cond-mat.mtrl-sci↗

arXiv2024

Automated Workflow for Accurate High-Throughput GW Calculations

The GW approximation represents the state-of-the-art ab-initio method for computing excited-state properties. Its execution requires control over a larger number of (often interdependent) parameters, and therefore its application in high-throughput studies is hindered by the intricate and time-consuming convergence process across a multi-dimensional parameter space. To address these challenges, here we develop a fully-automated open-source workflow for G$_0$W$_0$ calculations within the AiiDA-VASP plugin architecture. The workflow is based on an efficient estimation of the errors on the quasi-particle (QP) energies due to basis-set truncation and the pseudo-potential norm violation, which allows a reduction of the dimensionality of the parameter space and avoids the need for multi-dimensional convergence searches. Protocol validation is conducted through a systematic comparison against established experimental and state-of-the-art GW data. To demonstrate the effectiveness of the approach, we construct a database of QP energies for a diverse dataset of over 320 bulk structures. The openly accessible workflow and resulting dataset can serve as a valuable resource and reference for conducting accurate data-driven research.

cond-mat.mtrl-sci↗

arXiv2024

Optimizing ionic conductivity of lithium in Li$_7$PS$_6$ argyrodite via dopant engineering

Li-containing argyrodites represent a promising family of Li-ion conductors with several derived compounds exhibiting room-temperature ionic conductivity > 1 mS/cm and making them attractive as potential candidates as electrolytes in solid-state Li-ion batteries. Starting from the parent phase Li7PS6, several cation and anion substitution strategies have been attempted to increase the conductivity of Li ions. Nonetheless, a detailed understanding of the thermodynamics of native defects and doping of Li argyrodite and their effect on the ionic conductivity of Li is missing. Here, we report a comprehensive computational study of defect chemistry of the parent phase Li7PS6 in both intrinsic and extrinsic regimes, using a newly developed workflow to automate the computations of several defect formation energies in a thermodynamically consistent framework. Our findings agree with known experimental findings, rule out several unfavorable aliovalent dopants, narrowing down the potential promising candidates that can be tested experimentally. We also find that cation-anion co-doping can provide a powerful strategy to further optimize the composition of argyrodite. In particular, Si-F co-doping is predicted to be thermodynamically favorable; this could lead to the synthesis of the first F-doped Li-containing argyrodite. Finally, using DeePMD neural networks, we have mapped the ionic conductivity landscape as function of the concentration of the most promising cation and anion dopants identified from the defect calculations, and identified the most promising region in the compositional space with high Li conductivity that can be explored experimentally.

cond-mat.mtrl-sci↗

arXiv2024

First-principles characterization of thermal conductivity in LaPO4-based alloys

Alloys based on lanthanum phosphate (LaPO$_{4}$) are often employed as thermal barrier coatings, due to their low thermal conductivity and structural stability over a wide temperature range. To enhance the thermal-insulation performance of these alloys, it is essential to comprehensively understand the fundamental physics governing their heat conduction. Here, we employ the Wigner formulation of thermal transport in conjunction with first-principles calculations to elucidate how the interplay between anharmonicity and compositional disorder determines the thermal properties of La$_{1{-}x}$Gd$_{x}$PO$_{4}$ alloys, and discuss the fundamental physics underlying the emergence and coexistence of particle-like and wave-like heat-transport mechanisms. We also show how the Wigner transport equation describes correctly the thermodynamic limit of a compositionally disordered crystal, while the Boltzmann transport equation does not. Our predictions for microscopic vibrational properties (temperature-dependent Raman spectrum) and for macroscopic thermal conductivity are validated against experiments. Finally, we leverage these findings to devise strategies to optimize the performance of thermal barrier coatings.

cond-mat.mtrl-sci↗

arXiv2024

A comprehensive screening of plasma-facing materials for nuclear fusion

Plasma-facing materials (PFMs) represent one of the most significant challenges for the design of future nuclear fusion reactors. Inside the reactor, the divertor will experience the harshest material environment: intense bombardment of neutrons and plasma particles coupled with large and intermittent heat fluxes. The material designated to cover this role in ITER is tungsten (W). While no other materials have shown the potential to match the properties of W, many drawbacks associated with its application remain, including: cracking and erosion induced by a low recrystallization temperature combined with a high ductile-brittle transition temperature and neutron-initiated embrittlement; surface morphology changes (fuzz layer) due to plasma-W interaction with subsequent risk of spontaneous material melting and delamination; low oxidation resistance. This work aims to produce a structured and comprehensive materials screening of PFMs candidates based on known inorganic materials. The methodology applied in this study to identify the most promising PFM candidates combines peer-reviewed data present in the Pauling File database and DFT calculations of two key PFMs defects, namely the surface binding energy and the formation energy of a hydrogen interstitial. The crystal structures and their related properties, extracted from the Pauling File, are ranked according to the heat-balance equation of a PFM subject to the heat loads in the divertor region of an ITER-like tokamak. The materials satisfying the requirements are critically compared with the state-of-the-art literature, defining an optimal subset where to perform the first-principles electronic structure calculations. The majority of previously known PFMs are captured by this screening process, confirming its reliability. Additionally, less familiar refractory materials suggest performance that calls for further investigations.

cond-mat.mtrl-sci↗

arXiv2024

Energies and spectra of solids from the algorithmic inversion of dynamical Hubbard functionals

Energy functionals of the Green's function can simultaneously provide spectral and thermodynamic properties of interacting electrons' systems. Though powerful in principle, these formulations need to deal with dynamical (frequency-dependent) quantities, increasing the algorithmic and numerical complexity and limiting applications. We first show that, when representing all frequency-dependent propagators as sums over poles, the typical operations of dynamical formulations become closed (i.e., all quantities are expressed as sums over poles) and analytical. Importantly, we map the Dyson equation into a nonlinear eigenvalue problem that can be solved exactly; this is achieved by introducing a fictitious non-interacting system with additional degrees of freedom which shares, upon projection, the same Green's function of the real system. Last, we introduce an approximation to the exchange-correlation part of the Klein functional adopting a localized $GW$ approach; this is a generalization of the static Hubbard extension of density-functional theory with a dynamical screened potential $U(ω)$. We showcase the algorithmic efficiency of the methods, and the physical accuracy of the functional, by computing the spectral, thermodynamic, and vibrational properties of SrVO$_3$, finding results in close agreement with experiments and state-of-the-art methods, at highly reduced computational costs and with a transparent physical interpretation.

cond-mat.mtrl-sci↗

arXiv2024

Temperature-invariant heat conductivity from compensating crystalline and glassy transport: from the Steinbach meteorite to furnace bricks

The thermal conductivities of crystals and glasses vary strongly and with opposite trends upon heating, decreasing in crystals and increasing in glasses. Here, we show--both with first-principles predictions based on the Wigner transport equation and with thermoreflectance experiments--that the dominant transport mechanisms of crystals (particle-like propagation) and glasses (wave-like tunnelling) can coexist and compensate in materials with crystalline bond order and nearly glassy bond geometry. We demonstrate that ideal compensation emerges in a sample of silica in the form of tridymite, carved from a meteorite found in Steinbach (Germany) in 1724, and yields a "propagation-tunneling-invariant" (PTI) conductivity that is independent from temperature and intermediate between the opposite trends of $α$-quartz crystal and silica glass. We show how such PTI conductivity occurs in the quantum regime below the Debye temperature, and can largely persist at high temperatures in a geometrically amorphous tridymite phase found in refractory bricks fired for years in furnaces for steel smelting. Last, we discuss implications to heat transfer in solids exposed to extreme temperature variations, ranging from planetary cooling to heating protocols to reduce the carbon footprint of industrial furnaces.

cond-mat.mtrl-sci↗

arXiv2024

Potential energy surfaces from many-body functionals: analytical benchmarks and conserving many-body approximations

We investigate analytically the performance of many-body energy functionals, derived respectively by Klein and Luttinger and Ward, at different levels of diagrammatic approximations, ranging from second Born, to GW, to the so-called T-matrix, for the calculation of total energies and potential energy surfaces. We benchmark our theoretical results on the extended two-site Hubbard model, which is analytically solvable and for which several exact properties can be calculated. Despite its simplicity, this model displays the physics of strongly correlated electrons: it is prototypical of the H$_2$ dissociation, a notoriously difficult problem to solve accurately for the majority of mean-field based approaches. We show that both functionals exhibit good to excellent variational properties, particularly in the case of the Luttinger-Ward one, which is in close agreement with fully self-consistent calculations, and elucidate the relation between the accuracy of the results and the different input one-body Green's functions. Provided that these are wisely chosen, we show how the Luttinger-Ward functional can be used as a computationally inexpensive alternative to fully self-consistent many-body calculations, without sacrificing the precision of the results obtained. Furthermore, in virtue of this accuracy, we argue that this functional can also be used to rank different many-body approximations at different regimes of electronic correlation, once again bypassing the need for self-consistency.

cond-mat.str-el↗

arXiv2024

Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange

The Open Databases Integration for Materials Design (OPTIMADE) application programming interface (API) empowers users with holistic access to a growing federation of databases, enhancing the accessibility and discoverability of materials and chemical data. Since the first release of the OPTIMADE specification (v1.0), the API has undergone significant development, leading to the upcoming v1.2 release, and has underpinned multiple scientific studies. In this work, we highlight the latest features of the API format, accompanying software tools, and provide an update on the implementation of OPTIMADE in contributing materials databases. We end by providing several use cases that demonstrate the utility of the OPTIMADE API in materials research that continue to drive its ongoing development.

cond-mat.mtrl-sci↗

arXiv2024

Spectral Operator Representations

Machine learning in atomistic materials science has grown to become a powerful tool, with most approaches focusing on atomic arrangements, typically decomposed into local atomic environments. This approach, while well-suited for machine-learned interatomic potentials, is conceptually at odds with learning complex intrinsic properties of materials, often driven by spectral properties commonly represented in reciprocal space (e.g., band gaps or mobilities) which cannot be readily atomically partitioned. For such applications, methods which represent the electronic rather than the atomic structure could be more promising. In this work, we present a general framework focused on electronic-structure descriptors which take advantage of the natural symmetries and inherent interpretability of physical models. Using this framework, we formulate two such representations and apply them respectively to measuring the similarity of carbon nanotubes and barium titanate polymorphs, and to the discovery of novel transparent conducting materials (TCMs) in the Materials Cloud 3D database (MC3D). A random forest classifier trained on 1% of the materials in the MC3D is able to correctly label 76% of entries in database which meet common screening criteria for promising TCMs.

cond-mat.mtrl-sci↗

arXiv2024

Searching for the thinnest metallic wire

One-dimensional materials have gained much attention in the last decades: from carbon nanotubes to ultrathin nanowires, to few-atom atomic chains, these can all display unique electronic properties and great potential for next-generation applications. Exfoliable bulk materials could naturally provide a source for one-dimensional wires with well defined structure and electronics. Here, we explore a database of one-dimensional materials that could be exfoliated from experimentally known three-dimensional Van-der-Waals compounds, searching metallic wires that are resilient to Peierls distortions and could act as vias or interconnects for future downscaled electronic devices. As the one-dimensional nature makes these wires particularly susceptible to dynamical instabilities, we carefully characterise vibrational properties to identify stable phases and characterize electronic and dynamical properties. Our search identifies several novel and stable wires; notably, we identify what could be the thinnest possible exfoliable metallic wire, CuC$_2$, coming a step closer to the ultimate limit in materials downscaling.

cond-mat.mtrl-sci↗