Materials Cloud, a platform for open computational science

Abstract

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.

19 pages, 8 figures

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Cited by in corpus (83)

• Recent Advances and Applications of Deep Learning Methods in Materials Science
• AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance
• FAIR data enabling new horizons for materials research
• Workflows in AiiDA: Engineering a high-throughput, event-based engine for robust and modular computational workflows
• Accelerating computational materials discovery with artificial intelligence and cloud high-performance computing: from large-scale screening to experimental validation
• OPTIMADE, an API for exchanging materials data
• AiiDAlab -- an ecosystem for developing, executing, and sharing scientific workflows
• How to verify the precision of density-functional-theory implementations via reproducible and universal workflows
• Tutorial: How to Train a Neural Network Potential
• Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis
• Electronic Structure of Cesium-based Photocathode Materials from Density Functional Theory: Performance of PBE, SCAN, and HSE06 functionals
• AI-driven inverse design of materials: Past, present and future
• Automated high-throughput Wannierisation
• aflow.org: A Web Ecosystem of Databases, Software and Tools
• Viscosity in water from first-principles and deep-neural-network simulations
• Bayesian Force Fields from Active Learning for Simulation of Inter-Dimensional Transformation of Stanene
• High-accuracy thermodynamic properties to the melting point from ab initio calculations aided by machine-learning potentials
• Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange
• Common workflows for computing material properties using different quantum engines
• Temperature- and vacancy-concentration-dependence of heat transport in Li${}_3$ClO from multi-method numerical simulations
• Minimal set of crystallographic descriptors for sorption properties in hypothetical Metal Organic Frameworks: Role in sequential learning optimization
• A rule-free workflow for the automated generation of databases from scientific literature
• Hydrodynamic finite-size scaling of the thermal conductivity in glasses
• Robustness of Local Predictions in Atomistic Machine Learning Models
• Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectorie
• AlabOS: A Python-based Reconfigurable Workflow Management Framework for Autonomous Laboratories
• Two-dimensional Weyl points and nodal lines in pentagonal materials and their optical response
• Invariance principles in the theory and computation of transport coefficients
• The Wannier Function Software Ecosystem for Materials Simulations
• Machine Learning and Data-Driven Methods in Computational Surface and Interface Science
• Experimental and theoretical study of stable and metastable phases in sputtered CuInS${}_2$
• Projectability disentanglement for accurate and automated electronic-structure Hamiltonians
• Remote free-carrier screening to boost the mobility of Fröhlich-limited 2D semiconductors
• Density functional Bogoliubov-de Gennes analysis of superconducting Nb and Nb(110) surfaces
• Microscopic picture of paraelectric perovskites from structural prototypes
• Machine-learning accelerated identification of exfoliable two-dimensional materials
• Towards high-throughput many-body perturbation theory: efficient algorithms and automated workflows
• divERGe implements various Exact Renormalization Group examples
• 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
• Quantum optics measurement scheme for quantum geometry and topological invariants
• Thermal conductivity of Li${}_3$PS${}_4$ solid electrolytes with ab initio accuracy
• Screening Mixed-Metal Sn${}_2$M(III)Ch${}_2$X${}_3$ Chalcohalides for Photovoltaic Applications
• High throughput inverse design and Bayesian optimization of functionalities: spin splitting in two-dimensional compounds
• Topological Data Analysis of Ion Migration Mechanism
• Predicting hot-electron free energies from ground-state data
• Atomic diffusion-induced polarization and superconductivity in topological insulator-based heterostructures
• OSSCAR, an open platform for collaborative development of computational tools for education in science
• Single-photon emitters in WSe${}_2$: Critical role of phonons on excitation schemes and indistinguishability
• High-throughput screening of Weyl semimetals
• Prediction rigidities for data-driven chemistry
• Hubbard-corrected oxide formation enthalpies without adjustable parameters
• Materials Expert-Artificial Intelligence for Materials Discovery
• Adaptive energy reference for machine-learning models of the electronic density of states
• Optical materials discovery and design with federated databases and machine learning
• Efficient Implementation of Gaussian Process Regression Accelerated Saddle Point Searches with Application to Molecular Reactions
• Inter-orbital Cooper pairing at finite energies in Rashba surface states
• Accelerating the discovery of high-performance nonlinear optical materials using active learning and high-throughput screening
• 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
• Accelerating Discovery of Extreme Lattice Thermal Conductivity by Crystal Attention Graph Neural Network (CATGNN) Using Chemical Bonding Intuitive Descriptors
• Self-interaction and transport of solvated electrons in molten salts
• Maximum volume simplex method for automatic selection and classification of atomic environments and environment descriptor compression
• Automated computational workflows for muon spin spectroscopy
• mkite: A distributed computing platform for high-throughput materials simulations
• Energy-GNoME: A Living Database of Selected Materials for Energy Applications
• Bayesian Hierarchical Models for Quantitative Estimates for Performance metrics applied to Saddle Search Algorithms
• MC3D: The Materials Cloud computational database of experimentally known stoichiometric inorganics
• Phonon density of states of magnetite (\ce{Fe3O4}) nanoparticles via molecular dynamics simulations
• The AiiDA-Spirit plugin for automated spin-dynamics simulations and multi-scale modelling based on first-principles calculations
• Heat conductivity from energy-density fluctuations
• Intrinsic inverse band gap versus polarization relation in ferroelectric materials
• Implicit Neural Representations for Chemical Reaction Paths
• Datatractor: Metadata, automation, and registries for extractor interoperability in the chemical and materials sciences
• Robust Wannierization including magnetization and spin-orbit coupling via projectability disentanglement
• Spectral Operator Representations
• Investigating finite-size effects in molecular dynamics simulations of ion diffusion, heat transport, and thermal motion in superionic materials
• Robust and effective ab initio molecular dynamics simulations on the GPU cloud infrastructure using the Schrödinger Materials Science Suite
• Making atomistic materials calculations accessible with the AiiDAlab Quantum ESPRESSO app
• Resolving the Body-Order Paradox of Machine Learning Interatomic Potentials
• Representing spherical tensors with scalar-based machine-learning models
• "Fraternal-twin" ferroelectricity: competing polar states in hydrogen-doped samarium nickelate from first principles
• Score-based diffusion models for accurate crystal-structure inpainting and reconstruction of hydrogen positions