Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange

Abstract

The Open Databases Integration for Materials Design (OPTIMADE) application programming interface (API) empowers users with holistic access to a growing federation of databases, enhancing the accessibility and discoverability of materials and chemical data. Since the first release of the OPTIMADE specification (v1.0), the API has undergone significant development, leading to the upcoming v1.2 release, and has underpinned multiple scientific studies. In this work, we highlight the latest features of the API format, accompanying software tools, and provide an update on the implementation of OPTIMADE in contributing materials databases. We end by providing several use cases that demonstrate the utility of the OPTIMADE API in materials research that continue to drive its ongoing development.

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Cited by in corpus (12)

• Data-driven Design of High Pressure Hydride Superconductors using DFT and Deep Learning
• Optical materials discovery and design with federated databases and machine learning
• Accelerating the discovery of high-performance nonlinear optical materials using active learning and high-throughput screening
• Efficient Structure-Informed Featurization and Property Prediction of Ordered, Dilute, and Random Atomic Structures
• A Python workflow definition for computational materials design
• MC3D: The Materials Cloud computational database of experimentally known stoichiometric inorganics
• Datatractor: Metadata, automation, and registries for extractor interoperability in the chemical and materials sciences
• MADAS -- A Python framework for assessing similarity in materials-science data
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• Making atomistic materials calculations accessible with the AiiDAlab Quantum ESPRESSO app
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