AiiDA: Automated Interactive Infrastructure and Database for Computational Science

Abstract

Computational science has seen in the last decades a spectacular rise in the scope, breadth, and depth of its efforts. Notwithstanding this prevalence and impact, it is often still performed using the renaissance model of individual artisans gathered in a workshop, under the guidance of an established practitioner. Great benefits could follow instead from adopting concepts and tools coming from computer science to manage, preserve, and share these computational efforts. We illustrate here our paradigm sustaining such vision, based around the four pillars of Automation, Data, Environment, and Sharing. We then discuss its implementation in the open-source AiiDA platform (http://www.aiida.net), that has been tuned first to the demands of computational materials science. AiiDA's design is based on directed acyclic graphs to track the provenance of data and calculations, and ensure preservation and searchability. Remote computational resources are managed transparently, and automation is coupled with data storage to ensure reproducibility. Last, complex sequences of calculations can be encoded into scientific workflows. We believe that AiiDA's design and its sharing capabilities will encourage the creation of social ecosystems to disseminate codes, data, and scientific workflows.

30 pages, 7 figures

References in corpus (10)

• Quantum ESPRESSO: a modular and open-source software project for quantum simulations of materials
• The NumPy array: a structure for efficient numerical computation
• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• A real-space grid implementation of the Projector Augmented Wave method
• High-throughput electronic band structure calculations: challenges and tools
• Pseudopotentials for high-throughput DFT calculations
• Ab initio Random Structure Searching
• Predicting Crystal Structures with Data Mining of Quantum Calculations
• Koopmans-compliant functionals and their performance against reference molecular data
• Projector augmented-wave and all-electron calculations across the periodic table: a comparison of structural and energetic properties

Cited by in corpus (137)

• Advanced capabilities for materials modelling with Quantum ESPRESSO
• CP2K: An Electronic Structure and Molecular Dynamics Software Package -- Quickstep: Efficient and Accurate Electronic Structure Calculations
• Quantum ESPRESSO toward the exascale
• Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds
• Wannier90 as a community code: new features and applications
• A Universal Graph Deep Learning Interatomic Potential for the Periodic Table
• DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models
• Precision and efficiency in solid-state pseudopotential calculations
• Band structure diagram paths based on crystallography
• Comparing molecules and solids across structural and alchemical space
• Data-driven materials science: status, challenges and perspectives
• Big-Data Science in Porous Materials: Materials Genomics and Machine Learning
• Engineering of robust topological quantum phases in graphene nanoribbons
• SIESTA: recent developments and applications
• The Joint Automated Repository for Various Integrated Simulations (JARVIS) for data-driven materials design
• Many-body perturbation theory calculations using the yambo code
• Materials Cloud, a platform for open computational science
• Performance of arsenene and antimonene double-gate MOSFETs from first principles
• TB2J: a python package for computing magnetic interaction parameters
• Symmetry-Adapted Machine-Learning for Tensorial Properties of Atomistic Systems
• AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance
• Symbolic Regression in Materials Science
• Achieving DFT accuracy with a machine-learning interatomic potential: thermomechanics and defects in bcc ferromagnetic iron
• GPAW: An open Python package for electronic-structure calculations
• A critical examination of compound stability predictions from machine-learned formation energies
• icet - A Python library for constructing and sampling alloy cluster expansions
• Perovskite-Inspired Materials for Photovoltaics -- From Design to Devices
• Giant magnetic exchange coupling in rhombus-shaped nanographenes with zigzag periphery
• Spirit: Multifunctional Framework for Atomistic Spin Simulations
• HP -- A code for the calculation of Hubbard parameters using density-functional perturbation theory
• Unsupervised machine learning in atomistic simulations, between predictions and understanding
• Simple Data and Workflow Management with the signac Framework
• High-throughput computational screening for solid-state Li-ion conductors
• Thermal transport in crystals as a kinetic theory of relaxons
• Mobility of 2D materials from first principles in an accurate and automated framework
• Prediction of a large-gap and switchable Kane-Mele quantum spin Hall insulator
• Data-driven discovery of novel 2D materials by deep generative models
• The role of decomposition reactions in assessing first-principles predictions of solid stability
• Comparison of computational methods for the electrochemical stability window of solid-state electrolyte materials
• OPTIMADE, an API for exchanging materials data
• On-surface synthesis of antiaromatic and open-shell indeno[2,1-b]fluorene polymers and their lateral fusion into porous ribbons
• AiiDAlab -- an ecosystem for developing, executing, and sharing scientific workflows
• Abundance of ${\mathbb{Z}}_2$ topological order in exfoliable two-dimensional insulators
• How to verify the precision of density-functional-theory implementations via reproducible and universal workflows
• The PSML format and library for norm-conserving pseudopotential data curation and interoperability
• Thermodynamics and dielectric response of ${\text{BaTiO}}_3$ by data-driven modeling
• Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis
• Screening 2D materials with topological flat bands
• Bulk and surface electronic structure of the dual-topology semimetal Pt2HgSe3
• Automated high-throughput Wannierisation
• Recognizing Local and Global Structural Motifs at the Atomic Scale
• Material Descriptors for the Discovery of Efficient Thermoelectrics
• Automated construction of symmetrized Wannier-like tight-binding models from ab initio calculations
• Boltzmann Transport in Nanostructures as a Friction Effect
• Modeling lithium-ion solid-state electrolytes with a pinball model
• Accurate electronic properties and intercalation voltages of olivine-type Li-ion cathode materials from extended Hubbard functionals
• Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange
• The MAterials Simulation Toolkit (MAST) for atomistic modeling of defects and diffusion
• Benchmark and application of unsupervised classification approaches for univariate data
• Machine-learning structural reconstructions for accelerated point defect calculations
• Realistic finite temperature simulations of magnetic systems using quantum statistics
• Electronic structure calculations of twisted multi-layer graphene superlattices
• Towards fully automatized GW band structure calculations: What we can learn from 60.000 self-energy evaluations
• Semantic interoperability and characterization of data provenance in computational molecular engineering
• Common workflows for computing material properties using different quantum engines
• Combining phonon accuracy with high transferability in Gaussian approximation potential models
• Extensible Structure-Informed Prediction of Formation Energy with Improved Accuracy and Usability employing Neural Networks
• Accurate $abinitio$ tight-binding Hamiltonians: effective tools for electronic transport and optical spectroscopy from first principles
• Multiple antiferromagnetic phases and magnetic anisotropy in exfoliated CrBr${}_3$ multilayers
• The CECAM Electronic Structure Library and the modular software development paradigm
• A Quantum-Chemical Bonding Database for Solid-State Materials
• Unsupervised landmark analysis for jump detection in molecular dynamics simulations
• Electronic and optical properties of doped TiO2 by many-body perturbation theory
• Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectorie
• Photorealistic modelling of metals from first principles
• A posteriori metadata from automated provenance tracking: Integration of AiiDA and TCOD
• Machine Learning and Data-Driven Methods in Computational Surface and Interface Science
• The Wannier Function Software Ecosystem for Materials Simulations
• High-throughput ab initio reaction mechanism exploration in the cloud with automated multi-reference validation
• Transport Waves as Crystal Excitations
• Projectability disentanglement for accurate and automated electronic-structure Hamiltonians
• Convergence and pitfalls of density functional perturbation theory phonons calculations from a high-throughput perspective
• Remote free-carrier screening to boost the mobility of Fröhlich-limited 2D semiconductors
• Magnetism and stability of all primitive stacking patterns in bilayer chromium trihalides
• Computational Design of Moiré Assemblies Aided by Artificial Intelligence
• Microscopic picture of paraelectric perovskites from structural prototypes
• Tree based machine learning framework for predicting ground state energies of molecules
• Towards high-throughput many-body perturbation theory: efficient algorithms and automated workflows
• Automated mixing of maximally localized Wannier functions into target manifolds
• The AiiDA-KKR plugin and its application to high-throughput impurity embedding into a topological insulator
• Exploring the Cs-Te phase space via high-throughput density-functional theory calculations beyond the generalized-gradient approximation
• Automated coordination corrected enthalpies with AFLOW-CCE
• Screening Mixed-Metal Sn${}_2$M(III)Ch${}_2$X${}_3$ Chalcohalides for Photovoltaic Applications
• Data-driven analysis of the electronic-structure factors controlling the work functions of perovskite oxides
• Sim2Ls: FAIR simulation workflows and data
• High-throughput screening of Weyl semimetals
• The JARVIS Infrastructure is All You Need for Materials Design
• LO-mode phonon of KCl and NaCl at 300 K by inelastic X ray scattering measurements and first principles calculations
• First-principles Hubbard parameters with automated and reproducible workflows
• The solid-state Li-ion conductor Li${}_7$TaO${}_6$: A combined computational and experimental study
• Motif based high-throughput structure prediction of superconducting monolayer titanium boride
• HTESP (High-throughput electronic structure package): a Package for the high-throughput $ab$ $initio$ calculations
• Electronic properties of binary compounds with high fidelity and high throughput
• The type-I antiferromagnetic Weyl semimetal InMnTi${}_2$
• Towards machine learning for microscopic mechanisms: a formula search for crystal structure stability based on atomic properties
• A Python workflow definition for computational materials design
• A complementary screening for quantum spin Hall insulators in 2D exfoliable materials
• Automated computational workflows for muon spin spectroscopy
• Integrated workflows and interfaces for data-driven semi-empirical electronic structure calculations
• mkite: A distributed computing platform for high-throughput materials simulations
• MC3D: The Materials Cloud computational database of experimentally known stoichiometric inorganics
• Parameterization of the Stoner-Wohlfarth model of magnetic hysteresis
• The AiiDA-Spirit plugin for automated spin-dynamics simulations and multi-scale modelling based on first-principles calculations
• Combining Hammett $\sigma$ constants for $\Delta$-machine learning and catalyst discovery
• FirecREST: RESTful API on Cray XC systems
• Phonon structure of titanium under shear deformation along $\{10\bar{1}2\}$ twinning mode
• Open-Science Platform for Computational Materials Science: AiiDA and the Materials Cloud
• Semi-metallicity and electron-hole liquid in two-dimensional C and BN based compounds
• Efficacious symmetry-adapted atomic displacement method for lattice dynamical studies
• CateCom: a practical data-centric approach to categorization of computational models
• Spectral Operator Representations
• MADAS -- A Python framework for assessing similarity in materials-science data
• Toward Common Components for Open Workflow Systems
• Robust Wannierization including magnetization and spin-orbit coupling via projectability disentanglement
• Facilitating the sharing of electrophysiology data analysis results through in-depth provenance capture
• What can one learn about material structure given a single first-principles calculation?
• AFLOW$\pi$: A minimalist approach to high-throughput ab initio calculations including the generation of tight-binding hamiltonians
• kiwiPy: Robust, high-volume, messaging for big-data and computational science workflows
• Applying Distributed Ledgers to Manage Workflow Provenance
• Making atomistic materials calculations accessible with the AiiDAlab Quantum ESPRESSO app
• M-CODE: Materials Categorization via Ontology, Dimensionality and Evolution
• GEWUM: General Exploration Workflow for the Utopia of Materials: A Unified Platform for Automated Structure Generation, Selection, and Validation
• $\text{express}$: extensible, high-level workflows for swifter $\text{ab initio}$ materials modeling
• Machine learning, phase stability, and disorder with the Automatic Flow Framework for Materials Discovery
• Quantitative trends in 8 physical properties of 115000 inorganic compounds gained by machine learning
• Score-based diffusion models for accurate crystal-structure inpainting and reconstruction of hydrogen positions
• AFLUX: The LUX materials search API for the AFLOW data repositories