Projectability disentanglement for accurate and automated electronic-structure Hamiltonians

Maximally-localized Wannier functions(MLWFs)are broadly used to characterize the electronic structure of materials.Generally,one can construct MLWFs describing isolated bands(e.g.valence bands of insulators)or entangled bands(e.g.valence and conduction bands of insulators,or metals).Obtaining accurate and compact MLWFs often requires chemical intuition and trial and error,a challenging step even for experienced researchers and a roadblock for high-throughput calculations.Here,we present an automated approach,projectability-disentangled Wannier functions(PDWFs),that constructs MLWFs spanning the occupied bands and their complement for the empty states,providing a tight-binding picture of optimized atomic orbitals in crystals.Key to the algorithm is a projectability measure for each Bloch state onto atomic orbitals,determining if that state should be kept identically,discarded,or mixed into the disentanglement.We showcase the accuracy on a test set of 200 materials,and the reliability by constructing 21,737 Wannier Hamiltonians.

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,an 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.

Automated mixing of maximally localized Wannier functions into target manifolds

Maximally localized Wannier functions(MLWFs)are widely used in electronic-structure calculations.We have recently developed automated approaches to generate MLWFs that represent natural tight-binding sets of atomic-like orbitals;these describe accurately both the occupied states and the complementary unoccupied ones.For many applications,it is required to use MLWFs that describe instead certain target groups of bands:the valence or the conduction bands,or correlated manifolds.Here,we start from these tight-binding sets of MLWFs,and mix them using a combination of parallel transport and maximal localization to construct manifold-remixed Wannier functions(MRWFs):these are orthogonal sets of MLWFs that fully and only span desired target submanifolds.The algorithm is simple and robust,and is showcased here in reference applications(silicon,MoS_(2),and SrVO_(3))and in a mid-throughput study of 77 insulators.

Automated high-throughput Wannierisation

Maximally-localised Wannier functions(MLWFs)are routinely used to compute from first-principles advanced materials properties that require very dense Brillouin zone integration and to build accurate tight-binding models for scale-bridging simulations.At the same time,high-throughput(HT)computational materials design is an emergent field that promises to accelerate reliable and cost-effective design and optimisation of new materials with target properties.The use of MLWFs in HT workflows has been hampered by the fact that generating MLWFs automatically and robustly without any user intervention and for arbitrary materials is,in general,very challenging.We address this problem directly by proposing a procedure for automatically generating MLWFs for HT frameworks.Our approach is based on the selected columns of the density matrix method and we present the details of its implementation in an AiiDA workflow.We apply our approach to a dataset of 200 bulk crystalline materials that span a wide structural and chemical space.We assess the quality of our MLWFs in terms of the accuracy of the band-structure interpolation that they provide as compared to the band-structure obtained via full first-principles calculations.Finally,we provide a downloadable virtual machine that can be used to reproduce the results of this paper,including all first-principles and atomistic simulations as well as the computational workflows.

Thermodynamics and dielectric response of 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(BaTiO_(3)),a prototypical ferroelectric.The model uses ab initio calculations as a 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 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 the dielectric response properties of the material in a full ab initio manner,again reproducing the correct qualitative experimental behavior.

Common workflows for computing material properties using different quantum engines

The prediction of material properties 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 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 them.We demonstrate how developing common interfaces for workflows that automatically compute material properties greatly simplifies interoperability and cross-verification.We introduce design rules for reusable,code-agnostic,workflow interfaces to compute well-defined material properties,which we implement for eleven quantum engines and use to compute various material properties.Each implementation encodes carefully selected simulation parameters and workflow logic,making the implementer’s expertise of the quantum engine directly available to nonexperts.All workflows are made available as open-source and full reproducibility of the workflows is guaranteed through the use of the AiiDA infrastructure.