Software & Data Resources
AQUAMI
Building a Materials Data Infrastructure: Opening New Pathways to Discovery and Innovation in Science and Engineering
Chemoresponsive Liquid Crystal Research Database
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DeepFRI
HybriD³ Materials Database
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MASTML_Metallic_Glass_Bulk_Modulus
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.
MASTML_Metallic_Glass_Debye_T
A scikit-learn Random Forest model predicting metallic glass debye temperature values from accessible elemental feature inputs is shared on the DLHub hosting site. The model is callable with the DLHub API.
MASTML_Metallic_Glass_Density
A scikit-learn Linear Regression model predicting metallic glass density values from accessible elemental feature inputs is shared on the DLHub hosting site. The model is callable with the DLHub API.
MASTML_Metallic_Glass_Poissons_Ratio
A scikit-learn Random Forest model predicting metallic glass poisson’s ratio values from accessible elemental feature inputs is shared on the DLHub hosting site. The model is callable with the DLHub API.
MASTML_Metallic_Glass_Shear_Modulus
A extreme gradient boosted trees (XGBoost) model predicting metallic glass shear modulus values from accessible elemental feature inputs is shared on the DLHub hosting site. The model is callable with the DLHub API.
MASTML_Metallic_Glass_Youngs_Modulus
A scikit-learn Gradient Boosted Trees model predicting metallic glass young’s modulus values from accessible elemental feature inputs is shared on the DLHub hosting site. The model is callable with the DLHub API.
Magnetic Materials Database
Materials Data Bank
Organic Crystals in Electronic and Light-Oriented Technologies
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PDFitc
Penn Glass Database
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Polymer Visualization
This Polymer Visualization software offers a user-friendly way to plot outputs from the Self-Consistent Field Theory (SCFT) calculations, allowing automatic plotting of 1D, 2D and 3D concentration profiles for multiblock systems. The code has been updated so that it can produce structure factors of the form S(q) where q is the magnitude of the scattering wave vector. This new capability is especially important for direct comparison with experimental results, which are often obtained by small-angle X-ray scattering (SAXS) and thus directly probe the structure factor of the material.
The Synthesis Project
Thermal MIT Materials Database
Thermoelectrics Design Lab
V2O5 Detector: a synthetic data-driven deep learning model