Software & Data Resources
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.
Thermal MIT Materials Database
Thermoelectrics Design Lab
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.
The Synthesis Project
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.
The Visualization Toolkit for Analyzing Defects in Materials (VTAnDeM)
VTAnDeM is a post-processing plotting toolkit for DFT calculations of defects in materials. The toolkit allows simultaneous visualization of interconnected thermodynamic and electronic properties of materials, including phase stability, defects, and carrier concentrations.
V2O5 Detector: a synthetic data-driven deep learning model
XtalOpt
XtalOpt is an open-source code, published under the “NEW” BSD License, designed to perform multi-objective evolutionary search for novel functional materials. Local optimizations can be performed using a variety of explicitly supported first-principles codes and classical force fields (e.g. VASP, Quantum-Espresso, Abinit, and GULP); while any desired total energy calculation tool (e.g., machine learning potentials) can be utilized through scripting.
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