Software & Data Resources

AQUAMI is an open source Python package and GUI which can automatically analyze micrographs and extract quantitative information to characterize microstructure features. (Also see related paper on this software tool.)

The availability of increasingly sophisticated experimental and computational tools provides scientists and engineers with new opportunities, but harnessing the vast amounts of data generated from these new approaches presents a challenge. Building a Materials Data Infrastructure, funded by the DMREF program, identifies and prioritizes these challenges, while also providing actionable recommendations for addressing them.

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This website presents key results of the joint efforts of Cornell University, Kent State University, and the University of Wisconsin-Madison to accelerate the design of chemoresponsive liquid crystalline systems that respond to targeted analytes, such as organophosphonates (e.g. DMMP), O3, Cl2, and formaldehyde.

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DeepFRI is a structure-based protein function prediction (and functional residue identification) method using Graph Convolutional Networks with Language Model features. DeepFRI is a product of the Computationally Driven-Genetically Engineered Materials (CD-GEM) project.

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The HybriD³ materials database provides a comprehensive collection of experimental and computational materials data for crystalline organic-inorganic compounds, predominantly based on the perovskite paradigm.

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Magnetic Materials Database provides a large array of datasets for magnetic compounds as well as magnetic clusters, with focus on rare-earth-free magnets. An Iowa State University effort, this database is specifically designed for data sciences and machine learning modelings.

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The Materials Data Bank (MDB) archives the 3D coordinates and chemical species of individual atoms in materials without assuming crystallinity determined by atomic electron tomography. The databank is designed to provide useful resources for research and education in studying the 3D atomic arrangements and associated material properties arising from non-crystalline structures, such as point defects, dislocations, grain boundaries, stacking faults and disorders. MDB is a product of the Design and Testing of Nanoalloy Catalysts in 3D Atomic Resolution project.

OCELOT is an online archive for Organic Crystals in Electronic and Light-Oriented Technologies. It is part of the Organic Semiconductors by Computationally Accelerated Refinement (OSCAR) project, which aims to accelerate the development of new electronic and energy materials by developing computational models to predict solid-state order for a common class of high-performance materials.

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PDFitc, a cloud web platform, can host applications for PDF analysis to help researchers study the local and nanoscale structure of nanostructured materials. The applications are designed to be powerful and easy to use and can, and will, be extended over time through community adoption and development.

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The Penn Glass Molecule Database features glassy molecules synthesized and characterized at the University of Pennsylvania. Most of these molecules have been synthesized with the aim to produce stable vapor-deposited glass films. This website serves as a database for all the work done on these systems both experimentally and computationally.

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The goal of the Synthesis Project is to advance computational learning around materials synthesis approaches by creating a predictive synthesis system for advanced materials design and processing—to do for materials synthesis what modern computational methods have done for materials properties.

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The TE Design Lab is a thermoelectrics-focused virtual platform for discovery and design of novel thermoelectric materials. The database contains calculated transport properties and thermoelectric performance rankings of 2701 materials.

This web-based interative tool provides a deep-learning-based image analysis for nanowires on the fly.