Codebase for compiling a database of materials syntheses
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Updated
Jun 12, 2017 - Python
Codebase for compiling a database of materials syntheses
Fitting module. Used for establishing functions/linkages between material descriptors and process or property parameters.
Segmentation module. Deals with the transition from raw imaging data to microstructure representations.
Dimensionality Reduction module. Deals with the identification of dominant descriptors of microstructure fro a large set of statistics.
Spatial Statistics module. Deals with the calculation of spatial statistics under variety of scenarios and definitions.
Codebase for Synthesis Project API server
Periodic Voronoi Tesselation of Crystals and Feature Extraction for Quantum Machine Learning in Python
Knowledge hub to share and learn the state-of-the-art developments in computer graphics.
A Deep Learning Model For Homogenization of Two-Phase High-Contrast Three-dimensional Materials
A deep learning based domain knowledge integration for small dataset
A Deep Learning Model For Localization of Two-Phase High-Contrast Three-dimensional Materials
Microstructural Materials Design Via Deep Adversarial Learning Methodology
A deep learning based domain knowledge integration for small dataset
Collection of papers on text mining for materials science
Computational experiments using SMACT for materials design
Reproducing classical machine learning models for predicting the heat capacity of solid inorganics based on Anthony Wang BestPractices repository.
Periodic Table of Elemental Nobility / Reactivity
Supplementary material accompanying Frey, N. C.; Akinwande, D.; Jariwala, D.; Shenoy, V. B. Machine Learning-Enabled Design of Point Defects in 2D Materials for Quantum and Neuromorphic Information Processing, ACS Nano (2020).
Learning to Predict Crystal Plasticity at the Nanoscale:Deep Residual Networks and Size Effects in UniaxialCompression Discrete Dislocation Simulations
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