Materials Cloud, a platform for open computational science

Materials Cloud is a platform designed to enable open and seamless sharing of resources for computational science, driven by applications in materials modelling. It hosts 1) archival and dissemination services for raw and curated data, together with their provenance graph, 2) modelling services and virtual machines, 3) tools for data analytics, and pre-/post-processing, and 4) educational materials. Data is citable and archived persistently, providing a comprehensive embodiment of the FAIR principles that extends to computational workflows. Materials Cloud leverages the AiiDA framework to record the provenance of entire simulation pipelines (calculations performed, codes used, data generated) in the form of graphs that allow to retrace and reproduce any computed result. When an AiiDA database is shared on Materials Cloud, peers can browse the interconnected record of simulations, download individual files or the full database, and start their research from the results of the original authors. The infrastructure is agnostic to the specific simulation codes used and can support diverse applications in computational science that transcend its initial materials domain.Comment: 19 pages, 8 figure

Automated all-functionals infrared and Raman spectra

Infrared and Raman spectroscopies are ubiquitous techniques employed in many experimental laboratories, thanks to their fast and non-destructive nature able to capture materials' features as spectroscopic fingerprints. Nevertheless, these measurements frequently need theoretical support in order to unambiguously decipher and assign complex spectra. Linear-response theory provides an effective way to obtain the higher-order derivatives needed, but its applicability to modern exchange-correlation functionals remains limited. Here, we devise an automated, open-source, user-friendly approach based on ground-state density-functional theory and the electric enthalpy functional to allow seamless calculations of first-principles infrared and Raman spectra. By employing a finite-displacement and finite-field approach, we allow for the use of any functional, as well as an efficient treatment of large low-symmetry structures. Additionally, we propose a simple scheme for efficiently sampling the Brillouin zone with different electric fields. To demonstrate the capabilities of our approach, we provide illustrations using the ferroelectric LiNbO$_3$ crystal as a paradigmatic example. We predict infrared and Raman spectra using various (semi)local, Hubbard corrected, and hybrid functionals. Our results also show how PBE0 and extended Hubbard functionals yield in this case the best match in term of peak positions and intensities, respectively

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

mkite: A distributed computing platform for high-throughput materials simulations

Advances in high-throughput simulation (HTS) software enabled computational databases and big data to become common resources in materials science. However, while computational power is increasingly larger, software packages orchestrating complex workflows in heterogeneous environments are scarce. This paper introduces mkite, a Python package for performing HTS in distributed computing environments. The mkite toolkit is built with the server-client pattern, decoupling production databases from client runners. When used in combination with message brokers, mkite enables any available client to perform calculations without prior hardware specification on the server side. Furthermore, the software enables the creation of complex workflows with multiple inputs and branches, facilitating the exploration of combinatorial chemical spaces. Software design principles are discussed in detail, highlighting the usefulness of decoupling simulations and data management tasks to diversify simulation environments. To exemplify how mkite handles simulation workflows of combinatorial systems, case studies on zeolite synthesis and surface catalyst discovery are provided. Finally, key differences with other atomistic simulation workflows are outlined. The mkite suite can enable HTS in distributed computing environments, simplifying workflows with heterogeneous hardware and software, and helping deployment of calculations at scale.Comment: preprint; code available soo

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

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

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

SimStack: An Intuitive Workflow Framework

Establishing a fundamental understanding of the nature of materials via computational simulation approaches requires knowledge from different areas, including physics, materials science, chemistry, mechanical engineering, mathematics, and computer science. Accurate modeling of the characteristics of a particular system usually involves multiple scales and therefore requires the combination of methods from various fields into custom-tailored simulation workflows. The typical approach to developing patch-work solutions on a case-to-case basis requires extensive expertise in scripting, command-line execution, and knowledge of all methods and tools involved for data preparation, data transfer between modules, module execution, and analysis. Therefore multiscale simulations involving state-of-the-art methods suffer from limited scalability, reproducibility, and flexibility. In this work, we present the workflow framework SimStack that enables rapid prototyping of simulation workflows involving modules from various sources. In this platform, multiscale- and multimodule workflows for execution on remote computational resources are crafted via drag and drop, minimizing the required expertise and effort for workflow setup. By hiding the complexity of high-performance computations on remote resources and maximizing reproducibility, SimStack enables users from academia and industry to combine cutting-edge models into custom-tailored, scalable simulation solutions