Software & Data Resources

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.

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.

A scikit-learn Gradient Boosted Trees model predicting metallic glass bulk modulus values from accessible elemental feature inputs is shared on the DLHub hosting site. The model is callable with the DLHub API.

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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.

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.

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.