arXiv subjects

Giovanni Pizzi

Publications and source records attributed to Giovanni Pizzi.

At least 37 records · Page 2Linked to original sources

arXiv2024

Automated computational workflows for muon spin spectroscopy

Positive muon spin rotation and relaxation spectroscopy is a well established experimental technique for studying materials. It provides a local probe that generally complements scattering techniques in the study of magnetic systems and represents a valuable alternative for materials that display strong incoherent scattering or neutron absorption. Computational methods can effectively quantify the microscopic interactions underlying the experimentally observed signal, thus substantially boosting the predictive power of this technique. Here, we present an efficient set of algorithms and workflows devoted to the automation of this task. In particular, we adopt the so-called DFT+μ procedure, where the system is characterised in the density functional theory (DFT) framework with the muon modeled as a hydrogen impurity. We devise an automated strategy to obtain candidate muon stopping sites, their dipolar interaction with the nuclei, and hyperfine interactions with the electronic ground state. We validate the implementation on well-studied compounds, showing the effectiveness of our protocol in terms of accuracy and simplicity of use

physics.comp-ph↗

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

Magnetostriction-Driven Muon Localization in an Antiferromagnetic Oxide

Magnetostriction drives a rhombohedral distortion in the cubic rock salt antiferromagnet MnO at the Néel temperature $T_{N}=118$ K. As an unexpected consequence we show that this distortion acts to localize the site of an implanted muon due to the accompanying redistribution of electron density. This lifts the degeneracy between equivalent sites, resulting in a single observed muon precession frequency. Above $T_{N}$, the muon instead becomes delocalized around a network of equivalent sites. Our first-principles simulations based on Hubbard-corrected density-functional theory and molecular dynamics are consistent with our experimental data and help to resolve a long-standing puzzle regarding muon data on MnO, as well as having wider applicability to other magnetic oxides.

cond-mat.str-el↗

arXiv2023

Shared Metadata for Data-Centric Materials Science

The expansive production of data in materials science, their widespread sharing and repurposing requires educated support and stewardship. In order to ensure that this need helps rather than hinders scientific work, the implementation of the FAIR-data principles (Findable, Accessible, Interoperable, and Reusable) must not be too narrow. Besides, the wider materials-science community ought to agree on the strategies to tackle the challenges that are specific to its data, both from computations and experiments. In this paper, we present the result of the discussions held at the workshop on "Shared Metadata and Data Formats for Big-Data Driven Materials Science". We start from an operative definition of metadata, and what features a FAIR-compliant metadata schema should have. We will mainly focus on computational materials-science data and propose a constructive approach for the FAIRification of the (meta)data related to ground-state and excited-states calculations, potential-energy sampling, and generalized workflows. Finally, challenges with the FAIRification of experimental (meta)data and materials-science ontologies are presented together with an outlook of how to meet them.

cond-mat.mtrl-sci↗

arXiv2023

Projectability disentanglement for accurate and automated electronic-structure Hamiltonians

Maximally-localized Wannier functions (MLWFs) are a powerful and broadly used tool to characterize the electronic structure of materials, from chemical bonding to dielectric response to topological properties. Most generally, one can construct MLWFs that describe isolated band manifolds, e.g. for the valence bands of insulators, or entangled band manifolds, e.g. in metals or describing both the valence and the conduction manifolds in insulators. Obtaining MLWFs that describe a target manifold accurately and with the most compact representation often requires chemical intuition and trial and error, a challenging step even for experienced researchers and a roadblock for automated high-throughput calculations. Here, we present a powerful approach that automatically provides MLWFs spanning the occupied bands and their natural complement for the empty states, resulting in Wannier Hamiltonian models that provide a tight-binding picture of optimized atomic orbitals in crystals. Key to the success of the algorithm is the introduction of a projectability measure for each Bloch state onto atomic orbitals (here, chosen from the pseudopotential projectors) that determines if that state should be kept identically, discarded, or mixed into a disentangling algorithm. We showcase the accuracy of our method by comparing a reference test set of 200 materials against the selected-columns-of-the-density-matrix algorithm, and its reliability by constructing Wannier Hamiltonians for 21737 materials from the Materials Cloud.

physics.comp-ph↗

arXiv2023

Automated mixing of maximally localized Wannier functions into target manifolds

Maximally localized Wannier functions (MLWFs) are widely used to construct first-principles tight-binding models that accurately reproduce the electronic structure of materials. Recently, robust and automated approaches to generate these MLWFs have emerged, leading to natural sets of atomic-like orbitals that describe both the occupied states and the lowest-lying unoccupied ones (when the latter can be meaningfully described by bonding/anti-bonding combinations of localized orbitals). For many applications, it is important to instead have MLWFs that describe only certain target manifolds separated in energy between them -- the occupied states, the empty states, or certain groups of bands. Here, we start from the full set of MLWFs describing simultaneously all the target manifolds, and then mix them using a combination of parallel transport and maximal localization to construct orthogonal sets of MLWFs that fully and only span the desired target submanifolds. The algorithm is simple and robust, and it is applied to some paradigmatic but non-trivial cases (the valence and conduction bands of silicon, the top valence band of MoS$_2$, the $3d$ and $t_{2g}$/$e_g$ bands of SrVO$_3$) and to a mid-throughput study of 77 insulators.

physics.comp-ph↗

arXiv2023

How to verify the precision of density-functional-theory implementations via reproducible and universal workflows

In the past decades many density-functional theory methods and codes adopting periodic boundary conditions have been developed and are now extensively used in condensed matter physics and materials science research. Only in 2016, however, their precision (i.e., to which extent properties computed with different codes agree among each other) was systematically assessed on elemental crystals: a first crucial step to evaluate the reliability of such computations. We discuss here general recommendations for verification studies aiming at further testing precision and transferability of density-functional-theory computational approaches and codes. We illustrate such recommendations using a greatly expanded protocol covering the whole periodic table from Z=1 to 96 and characterizing 10 prototypical cubic compounds for each element: 4 unaries and 6 oxides, spanning a wide range of coordination numbers and oxidation states. The primary outcome is a reference dataset of 960 equations of state cross-checked between two all-electron codes, then used to verify and improve nine pseudopotential-based approaches. Such effort is facilitated by deploying AiiDA common workflows that perform automatic input parameter selection, provide identical input/output interfaces across codes, and ensure full reproducibility. Finally, we discuss the extent to which the current results for total energies can be reused for different goals (e.g., obtaining formation energies).

cond-mat.mtrl-sci↗

arXiv2023

Towards high-throughput many-body perturbation theory: efficient algorithms and automated workflows

The automation of ab initio simulations is essential in view of performing high-throughput (HT) computational screenings oriented to the discovery of novel materials with desired physical properties. In this work, we propose algorithms and implementations that are relevant to extend this approach beyond density functional theory (DFT), in order to automate many-body perturbation theory (MBPT) calculations. Notably, a novel algorithm pursuing the goal of an efficient and robust convergence procedure for GW and BSE simulations is provided, together with its implementation in a fully automated framework. This is accompanied by an automatic GW band interpolation scheme based on maximally-localized Wannier functions, aiming at a reduction of the computational burden of quasiparticle band structures while preserving high accuracy. The proposed developments are validated on a set of representative semiconductor and metallic systems.

cond-mat.mtrl-sci↗

arXiv2022

Novel materials in the Materials Cloud 2D database

Two-dimensional (2D) materials are among the most promising candidates for beyond-silicon electronic, optoelectronic and quantum computing applications. Recently, their recognized importance sparked a push to discover and characterize novel 2D materials. Within a few years, the number of experimentally exfoliated or synthesized 2D materials went from a couple of dozens to more than a hundred, with the number of theoretically predicted compounds reaching a few thousands. In 2018 we first contributed to this effort with the identification of 1825 compounds that are either easily (1036) or potentially (789) exfoliable from experimentally known 3D compounds. Here, we report on a major expansion of this 2D portfolio thanks to the extension of the screening protocol to an additional experimental database (MPDS) as well as to the updated versions of the two databases (ICSD and COD) used in our previous work. This expansion has led to the discovery of additional 1252 unique monolayers, bringing the total to 3077 compounds and, notably, almost doubling the number of easily exfoliable materials (2004). Moreover, we optimized the structural properties of all these monolayers and explored their electronic structure with a particular emphasis on those rare large-bandgap 2D materials that could be precious to isolate 2D field effect transistors channels. Finally, for each material containing up to 6 atoms per unit cell, we identified the best candidates to form commensurate heterostructures, balancing requirements on the supercells size and minimal strain.

cond-mat.mtrl-sci↗

arXiv2022

Thermodynamics and dielectric response of $\text{BaTiO}_3$ by data-driven modeling

Modeling ferroelectric materials from first principles is one of the successes of density-functional theory, and the driver of much development effort, requiring an accurate description of the electronic processes and the thermodynamic equilibrium that drive the spontaneous symmetry breaking and the emergence of macroscopic polarization. We demonstrate the development and application of an integrated machine learning model that describes on the same footing structural, energetic and functional properties of barium titanate ($\text{BaTiO}_3$), a prototypical ferroelectric. The model uses ab initio calculations as reference and achieves accurate yet inexpensive predictions of energy and polarization on time and length scales that are not accessible to direct ab initio modeling. These predictions allow us to assess the microscopic mechanism of the ferroelectric transition. The presence of an order-disorder transition for the Ti off-centered states is the main driver of the ferroelectric transition, even though the coupling between symmetry breaking and cell distortions determines the presence of intermediate, partly-ordered phases. Moreover, we thoroughly probe the static and dynamical behavior of $\text{BaTiO}_3$ across its phase diagram, without the need to introduce a coarse-grained description of the ferroelectric transition. Finally, we apply the polarization model to calculate dielectric response properties of the material in a fully ab initio manner, again reproducing the correct qualitative experimental behaviour.

cond-mat.mtrl-sci↗

arXiv2022

Machine-learning accelerated identification of exfoliable two-dimensional materials

Two-dimensional (2D) materials have been a central focus of recent research because they host a variety of properties, making them attractive both for fundamental science and for applications. It is thus crucial to be able to identify accurately and efficiently if bulk three-dimensional (3D) materials are formed by layers held together by a weak binding energy that, thus, can be potentially exfoliated into 2D materials. In this work, we develop a machine-learning (ML) approach that, combined with a fast preliminary geometrical screening, is able to efficiently identify potentially exfoliable materials. Starting from a combination of descriptors for crystal structures, we work out a subset of them that are crucial for accurate predictions. Our final ML model, based on a random forest classifier, has a very high recall of 98\%. Using a SHapely Additive exPlanations (SHAP) analysis, we also provide an intuitive explanation of the five most important variables of the model. Finally, we compare the performance of our best ML model with a deep neural network architecture using the same descriptors. To make our algorithms and models easily accessible, we publish an online tool on the Materials Cloud portal that only requires a bulk 3D crystal structure as input. Our tool thus provides a practical yet straightforward approach to assess whether any 3D compound can be exfoliated into 2D layers.

cond-mat.mtrl-sci↗

arXiv2022

Microscopic picture of paraelectric perovskites from structural prototypes

We show with first-principles molecular dynamics the persistence of intrinsic $\langle111\rangle$ Ti off-centerings for BaTiO$_3$ in its cubic paraelectric phase. Intriguingly, these are inconsistent with the Pm$\bar 3$m space group often used to atomistically model this phase using density functional theory or similar methods. Therefore we deploy a systematic symmetry analysis to construct representative structural models in the form of supercells that satisfy a desired point symmetry but are built from the combination of lower-symmetry primitive cells. We define as structural prototypes the smallest of these that are both energetically and dynamically stable. Remarkably, two 40-atom prototypes can be identified for paraelectric BaTiO$_3$; these are also common to many other $AB$O$_3$ perovskites. These prototypes can offer structural models of paraelectric phases that can be used for the computational engineering of functional materials. Last, we show that the emergence of B-cation off-centerings and the primitive-cell phonon instabilities are controlled by the equilibrium volume, in turn dictated by the filler A cation.

cond-mat.mtrl-sci↗

arXiv2022

OSSCAR, an open platform for collaborative development of computational tools for education in science

In this paper we present the Open Software Services for Classrooms and Research (OSSCAR) platform. OSSCAR provides an open collaborative environment to develop, deploy and access educational resources in the form of web applications. To minimize efforts in the creation and use of new educational material, it combines software tools that have emerged as standards with custom domain-specific ones. The technical solutions adopted to create and distribute content are described and motivated on the basis of reliability, sustainability, ease of uptake and use. Examples from courses in the domains of physics, chemistry, and materials science are shown to demonstrate the style and level of interactivity of typical applications. The tools presented are easy to use, and create a uniform and open environment exploitable by a large community of teachers, students, and researchers with the goal of facilitating learning and avoiding, when possible, duplication of efforts in creating teaching material. Contributions to expand the educational content of the OSSCAR project are welcome.

physics.ed-ph↗

arXiv2021

OPTIMADE, an API for exchanging materials data

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

cond-mat.mtrl-sci↗

arXiv2021

Strain-induced polar discontinuities in two-dimensional materials from combined first-principles and Schrödinger-Poisson simulations

The local application of mechanical stress in piezoelectric materials gives rise to boundaries across which the electric polarization changes. Polarization charges appear along such polar discontinuities and the ensuing electric fields drive a charge reconstruction with the accumulation of free carriers at the boundaries. This is particularly relevant for two-dimensional materials that can sustain very large strains and display record piezoelectric responses. Here we show by first-principles simulations the emergence of one-dimensional wires of free electrons and holes along strain interfaces, taking SnSe as a paradigmatic material. We complement this by developing a Schrödinger-Poisson approach specifically designed for two-dimensional materials that it is able to reproduce the ab-initio results and also to extend them to regimes of parameters and system sizes that would be unaffordable in first principles calculations. This model allows us to assess the degree of tunability for the free charge in the wires coming from strain values and profiles, and to obtain the critical size at which the interfaces start to be metallic.

cond-mat.mes-hall↗

arXiv2021

Open-Science Platform for Computational Materials Science: AiiDA and the Materials Cloud

We discuss here our vision for an Open-Science platform for computational Materials Science. Such a platform needs to rely on three pillars, consisting of 1) open data generation tools (including the simulation codes, the scientific workflows and the infrastructure for automation and provenance-tracking); 2) an open integration platform where these tools interact in an easily accessible way and computations are coordinated by automated workflows; and 3) support for seamless code and data sharing through portals that are FAIR-compliant and compatible with data-management plans. As a practical implementation, we show how such a platform in a few examples and focusing in particular on the combination of the AiiDA infrastructure and the Materials Cloud web portal.

cond-mat.mtrl-sci↗

arXiv2021

Data Management Plans: the Importance of Data Management in the BIG-MAP Project

Open access to research data is increasingly important for accelerating research. Grant authorities therefore request detailed plans for how data is managed in the projects they finance. We have recently developed such a plan for the EU-H2020 BIG-MAP project - a cross-disciplinary project targeting disruptive battery-material discoveries. Essential for reaching the goal is extensive sharing of research data across scales, disciplines and stakeholders, not limited to BIG-MAP and the European BATTERY 2030+ initiative but within the entire battery community. The key challenges faced in developing the data management plan for such a large and complex project were to generate an overview of the enormous amount of data that will be produced, to build an understanding of the data flow within the project and to agree on a roadmap for making all data FAIR. This paper describes the process we followed and how we structured the plan.

cond-mat.mtrl-sci↗

arXiv2021

Common workflows for computing material properties using different quantum engines

The prediction of material properties through electronic-structure simulations based on density-functional theory has become routinely common, thanks, in part, to the steady increase in the number and robustness of available simulation packages. This plurality of codes and methods aiming to solve similar problems is both a boon and a burden. While providing great opportunities for cross-verification, these packages adopt different methods, algorithms, and paradigms, making it challenging to choose, master, and efficiently use any one for a given task. Leveraging recent advances in managing reproducible scientific workflows, we demonstrate how developing common interfaces for workflows that automatically compute material properties can tackle the challenge mentioned above, greatly simplifying interoperability and cross-verification. We introduce design rules for reproducible and reusable code-agnostic workflow interfaces to compute well-defined material properties, which we implement for eleven different quantum engines and use to compute three different material properties. Each implementation encodes carefully selected simulation parameters and workflow logic, making the implementer's expertise of the quantum engine directly available to non-experts. Full provenance and reproducibility of the workflows is guaranteed through the use of the AiiDA infrastructure. All workflows are made available as open-source and come pre-installed with the Quantum Mobile virtual machine, making their use straightforward.

cond-mat.mtrl-sci↗