Siesta: Recent developments and applications

A review of the present status, recent enhancements, and applicability of the SIESTA program is presented. Since its debut in the mid-1990s, SIESTA’s flexibility, efficiency, and free distribution have given advanced materials simulation capabilities to many groups worldwide. The core methodological scheme of SIESTA combines finite-support pseudo-atomic orbitals as basis sets, norm-conserving pseudopotentials, and a realspace grid for the representation of charge density and potentials and the computation of their associated matrix elements. Here, we describe the more recent implementations on top of that core scheme, which include full spin–orbit interaction, non-repeated and multiple-contact ballistic electron transport, density functional theory (DFT)+U and hybrid functionals, time-dependent DFT, novel reduced-scaling solvers, density-functional perturbation theory, efficient van der Waals non-local density functionals, and enhanced molecular-dynamics options. In addition, a substantial effort has been made in enhancing interoperability and interfacing with other codes and utilities, such as WANNIER90 and the second-principles modeling it can be used for, an AiiDA plugin for workflow automatization, interface to Lua for steering SIESTA runs, and various post-processing utilities. SIESTA has also been engaged in the Electronic Structure Library effort from its inception, which has allowed the sharing of various low-level libraries, as well as data standards and support for them, particularly the PSeudopotential Markup Language definition and library for transferable pseudopotentials, and the interface to the ELectronic Structure Infrastructure library of solvers. Code sharing is made easier by the new open-source licensing model of the program. This review also presents examples of application of the capabilities of the code, as well as a view of on-going and future developments. Published under license by AIP Publishing.Siesta development was historically supported by different Spanish National Plan projects (Project Nos. MEC-DGES-PB95-0202, MCyT-BFM2000-1312, MEC-BFM2003-03372, FIS2006-12117, FIS2009-12721, FIS2012-37549, FIS2015-64886-P, and RTC-2016-5681-7), the latter one together with Simune Atomistics Ltd. We are thankful for financial support from the Spanish Ministry of Science, Innovation and Universities through Grant No. PGC2018-096955-B. We acknowledge the Severo Ochoa Center of Excellence Program [Grant Nos. SEV-2015-0496 (ICMAB) and SEV-2017-0706 (ICN2)], the GenCat (Grant No. 2017SGR1506), and the European Union MaX Center of Excellence (EU-H2020 Grant No. 824143). P.G.-F. acknowledges support from Ramón y Cajal (Grant No. RyC-2013-12515). J.I.C. acknowledges Grant No. RTI2018-097895-B-C41. R.C. acknowledges the European Union’s Horizon 2020 Research and Innovation Program under Marie Skłodoswka-Curie Grant Agreement No. 665919. D.S.P, P.K., and P.B. acknowledge Grant No. MAT2016-78293-C6, FET-Open No. 863098, and UPV-EHU Grant No. IT1246-19. V. W. Yu was supported by a MolSSI Fellowship (U.S. NSF Award No. 1547580), and V.B. and V.W.Y. were supported by the ELSI Development by the NSF (Award No. 1450280). We also acknowledge Honghui Shang and Xinming Qin for giving us access to the honpas code, where a preliminary version of the hybrid functional support described here was implemented. We are indebted to other contributors to the Siesta project whose names can be seen in the Docs/Contributors.txt file of the Siesta distribution, and we thank those, too many to list, contributing fixes, comments, clarifications, and documentation for the code.Peer reviewe

Siesta: Recent developments and applications

A review of the present status, recent enhancements, and applicability of the Siesta program is presented. Since its debut in the mid-1990s, Siesta?s flexibility, efficiency, and free distribution have given advanced materials simulation capabilities to many groups worldwide. The core methodological scheme of Siesta combines finite-support pseudo-atomic orbitals as basis sets, norm-conserving pseudopotentials, and a real-space grid for the representation of charge density and potentials and the computation of their associated matrix elements. Here, we describe the more recent implementations on top of that core scheme, which include full spin?orbit interaction, non-repeated and multiple-contact ballistic electron transport, density functional theory (DFT)+U and hybrid functionals, time-dependent DFT, novel reduced-scaling solvers, density-functional perturbation theory, efficient van der Waals non-local density functionals, and enhanced molecular-dynamics options. In addition, a substantial effort has been made in enhancing interoperability and interfacing with other codes and utilities, such as wannier90 and the second-principles modeling it can be used for, an AiiDA plugin for workflow automatization, interface to Lua for steering Siesta runs, and various post-processing utilities. Siesta has also been engaged in the Electronic Structure Library effort from its inception, which has allowed the sharing of various low-level libraries, as well as data standards and support for them, particularly the PSeudopotential Markup Language definition and library for transferable pseudopotentials, and the interface to the ELectronic Structure Infrastructure library of solvers. Code sharing is made easier by the new open-source licensing model of the program. This review also presents examples of application of the capabilities of the code, as well as a view of on-going and future developments.SIESTA development was historically supported by different Spanish National Plan projects (Project Nos. MEC-DGES-PB95-0202, MCyT-BFM2000-1312, MEC-BFM2003-03372, FIS2006-12117, FIS2009-12721, FIS2012-37549, FIS2015-64886-P, and RTC-2016-5681-7), the latter one together with Simune Atomistics Ltd. We are thankful for financial support from the Spanish Ministry of Science, Innovation and Universities through Grant No. PGC2018-096955-

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).Comment: Main text: 23 pages, 4 figures. Supplementary: 68 page

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 non-experts. All workflows are made available as open-source and full reproducibility of the workflows is guaranteed through the use of the AiiDA infrastructure.This work is supported by the MARVEL National Centre of Competence in Research (NCCR) funded by the Swiss National Science Foundation (grant agreement ID 51NF40-182892) and by the European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 824143 (European MaX Centre of Excellence “Materials design at the Exascale”) and Grant Agreement No. 814487 (INTERSECT project). We thank M. Giantomassi and J.-M. Beuken for their contributions in adding support for PseudoDojo tables to the aiida-pseudo (https://github.com/aiidateam/aiida-pseudo) plugin. We also thank X. Gonze, M. Giantomassi, M. Probert, C. Pickard, P. Hasnip, J. Hutter, M. Iannuzzi, D. Wortmann, S. Blügel, J. Hess, F. Neese, and P. Delugas for providing useful feedback on the various quantum engine implementations. S.P. acknowledges support from the European Unions Horizon 2020 Research and Innovation Programme, under the Marie Skłodowska-Curie Grant Agreement SELPH2D No. 839217 and computer time provided by the PRACE-21 resources MareNostrum at BSC-CNS. E.F.-L. acknowledges the support of the Norwegian Research Council (project number 262339) and computational resources provided by Sigma2. P.Z.-P. thanks to the Faraday Institution CATMAT project (EP/S003053/1, FIRG016) for financial support. KE acknowledges the Swiss National Science Foundation (grant number 200020-182015). G.Pi. and K.E. acknowledge the swissuniversities “Materials Cloud” (project number 201-003). Work at ICMAB is supported by the Severo Ochoa Centers of Excellence Program (MICINN CEX2019-000917-S), by PGC2018-096955-B-C44 (MCIU/AEI/FEDER, UE), and by GenCat 2017SGR1506. B.Z. thanks to the Faraday Institution FutureCat project (EP/S003053/1, FIRG017) for financial support. J.B. and V.T. acknowledge support by the Joint Lab Virtual Materials Design (JLVMD) of the Forschungszentrum Jülich.Peer reviewe

Molecular dynamics simulations of asphaltene aggregation: machine learning identification of representative molecules, polydispersity and inhibitor performance

Molecular Dynamics simulations have been employed to investigate the effect of polydispersity on the aggregation of asphaltene. To make the large combinatorial space of possible asphaltene blends accessible to a systematic study via simulation, an upfront unsupervised machine learning approach (clustering) was employed to identify a reduced set of model molecules representative of the diversity of asphaltene. For these molecules, monodisperse asphaltene simulations have shown a broad range of aggregation behavior, driven by their structural features: size of the aromatic core, length of the aliphatic chains and presence of heteroatoms. Then, the combination of these model molecules in a series of polydisperse mixtures have highlighted the complex and diverse effects of polydispersity on the aggregation process of asphaltene, which yielded both antagonistic, synergistic and seed effects. These findings illustrate the necessity of accounting for polydispersity when studying the asphaltene aggregation process and have permitted to establish a robust protocol for the in-silico evaluation of the performance of asphaltene inhibitors, as illustrated for the case of a nonylphenol resin

Molecular Dynamics Simulations of Asphaltene Aggregation: Machine-Learning Identification of Representative Molecules, Molecular Polydispersity, and Inhibitor Performance

Molecular dynamics simulations have been employed to investigate the effect of molecular polydispersity on the aggregation of asphaltene. To make the large combinatorial space of possible asphaltene blends accessible to a systematic study via simulation, an upfront unsupervised machine-learning approach (clustering) was employed to identify a reduced set of model molecules representative of the diversity of asphaltene. For these molecules, single asphaltene model simulations have shown a broad range of aggregation behaviors, driven by their structural features: size of the aromatic core, length of the aliphatic chains, and presence of heteroatoms. Then, the combination of these model molecules in a series of mixtures have highlighted the complex and diverse effects of molecular polydispersity on the aggregation process of asphaltene. Simulations yielded both antagonistic and synergistic effects mediated by the trigger or facilitator action of specific asphaltene model molecules. These findings illustrate the necessity of accounting for molecular polydispersity when studying the asphaltene aggregation process and have permitted establishing a robust protocol for the in silico evaluation of the performance of asphaltene inhibitors, as illustrated for the case of a nonylphenol resin

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

Common workflows for computing material properties using different quantum engines

Abstract 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 non-experts. All workflows are made available as open-source and full reproducibility of the workflows is guaranteed through the use of the AiiDA infrastructure

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

Density-functional theory methods and codes adopting periodic boundary conditions are extensively used in condensed matter physics and materials science research. In 2016, their precision (how well 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. In this Expert Recommendation, we discuss 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: four unaries and six 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. Finally, we discuss the extent to which the current results for total energies can be reused for different goals

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

Density-functional theory methods and codes adopting periodic boundary conditions are extensively used in condensed matter physics and materials science research. In 2016, their precision (how well 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. In this Expert Recommendation, we discuss 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: four unaries and six 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. Finally, we discuss the extent to which the current results for total energies can be reused for different goals.This work was inspired and is supported in part by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 676598 and grant agreement No. 824143 (European MaX Centre of Excellence “Materials design at the Exascale”) and by NCCR MARVEL, a National Centre of Competence in Research, funded by the Swiss National Science Foundation (grant number 205602). For the purpose of Open Access, a CC BY public copyright licence is applied to any Author Accepted Manuscript (AAM) version arising from this submission. We acknowledge Flaviano José dos Santos for useful discussions on the analysis of the smearing types and k-point convergence, and Xavier Gonze, Marc Torrent, and François Jollet for useful discussions on PAW pseudopotentials. M.F. and N.M. acknowledge the contribution of Sadasivan Shankar in early discussions about the use of 6 prototype oxides as general platform to explore the transferability of pseudopotentials. Work at ICMAB (E.B., A.G., V.D.) is supported by the Severo Ochoa Centers of Excellence Program (MCIN CEX2019- 000917-S), by grant PGC2018-096955-B-C44 of MCIN/AEI/10.13039/501100011033, “ERDF A way of making Europe”, and by GenCat 2017SGR1506. We also thank the Barcelona Supercomputer Center for computational resources. V.D. acknowledges support from DOC-FAM, European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 754397. O.R. acknowledges travel support from WIEN2k (Technical University of Vienna). The Jülich team (S.B., J.B., H.J., G.M., D.W) acknowledges support by the Joint Lab Virtual Materials Design (JL-VMD) of the Forschungszentrum Jülich, the Helmholtz Platform for Research Software Engineering - Preparatory Study (HIRSE_PS), the Joint Virtual Laboratory AI, Data Analytics and Scalable Simulation (AIDAS) of the Forschungszentrum Jülich and the French Alternative Energies and Atomic Energy Commission, and we gratefully acknowledge the computing time granted through JARA on the supercomputers JURECA83 at Forschungszentrum Jülich and CLAIX at RWTH Aachen University. H.M and T.D.K (University of Paderborn) gratefully acknowledge the Gauss Centre for Supercomputing e.V. (www.gausscentre. eu) for funding this project by providing computing time on the GCS Supercomputer JUWELS at Jülich Supercomputing Centre (JSC). S.P. and G.-M.R. (Université catholique de Louvain) acknowledge support from the F.R.S.-FNRS. Computational resources have been provided by the PRACE-21 resources MareNostrum at BSC-CNS and by the Consortium des Équipements de Calcul Intensif (CÉCI), funded by the Fonds de la Recherche Scientifique de Belgique (F.R.S.-FNRS) under Grant No. 2.5020.11 and by the Walloon Region as well as computational resources awarded on the Belgian share of the EuroHPC LUMI supercomputer. G.Ka. and S.V. received funding from the VILLUM Centre for the Science of Sustainable Fuels and Chemicals (9455) from VILLUM FONDEN. Computational resources were provided by the Niflheim supercomputing cluster at the Technical University of Denmark (DTU). They also thank Jens Jørgen Mortensen and Ask H. Larsen for the valuable discussions on optimizing the workflow for the GPAW code. S.C. acknowledges financial support from OCAS NV by an OCAS-endowed chair at Ghent University. The computational resources and services used at Ghent University were provided the VSC (Flemish Supercomputer Center), funded by the Research Foundation Flanders (FWO) and the Flemish Government – department EWI. Accepted Manuscript by Nature Review Physics Version of Record at https://doi.org/10.1038/s42254-023-00655-3 19/94 M.W. gratefully acknowledges computational resources provided by the Vienna Scientific Cluster (VSC). This research was funded in part by the Austrian Science Fund (FWF) [P 32711]. E.F.L. would like to acknowledge resources provided by Sigma2 - the National Infrastructure for High Performance Computing and Data Storage in Norway and support from the Norwegian Research Infrastructure Services (NRIS). B.Z. is grateful to the UK Materials and Molecular Modelling Hub for computational resources, which is partially funded by EPSRC (EP/P020194/1 and EP/T022213/1) and acknowledge the use of the UCL Myriad and Kathleen High Performance Computing Facility (Myriad@UCL, Kathleen@UCL), and associated support services, in the completion of this work. N.M., G.Pi., and A.G. acknowledge support from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 957189 (BIG-MAP), also part of the BATTERY 2030+ initiative under grant agreement No. 957213. G.P., J.Y. and G.-M.R. acknowledge support by the Swiss National Science Foundation (SNSF) and by the Fonds de la Recherche Scientifique de Belgique (F.R.S.-FNRS) through the “FISH4DIET” Project (SNSF grant 200021E_206190 and F.R.S.-FNRS grant T.0179.22). G.P. acknowledges support by the Open Research Data Program of the ETH Board, under the Establish project “PREMISE”. J.Y. acknowledges support from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 760173 (MARKETPLACE).With funding from the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000917-S).Peer reviewe