uw-cmg Goto Github PK
Name: UW-Madison Computational Materials Group
Type: Organization
Location: University of Wisconsin-Madison
Name: UW-Madison Computational Materials Group
Type: Organization
Location: University of Wisconsin-Madison
Random forest model for predicting stability and T-dependent area specific resistance of perovskite catalysts
3D port of game prototypes
AtomTouch: App for Interactive Exploration of the Nanoworld
Agent-based sequential learning software for materials discovery
All models created by the Computational Materials Group (CMG) at the University of Wisconsin-Madison (UW-Madison) will reside here.
Loop defect detection project with deep learning framework
Defect STEM Video Analysis
MAterials Simulation Toolkit for use with pymatgen
MAterials Simulation Toolkit for Machine Learning (MAST-ML)
Monte Carlo Modeling of Secondary Electron Emission using fully DFT input
Vidit's work on error bar predictions.
A package for definining the domain of a machine learning model via a feature dissimilarity metric.
Medical Image Analysis Project
Codes for machine learning of Panccyst Cancer
Codes for Machine learning for interpreting coherent X-ray speckle patterns
This is the Cluster Dynamics code for evolution of Mn-Ni-Si in low-Cu RPV steels
Random forest model to predict the electronic bandgap of materials
Random forest model to predict the compressive strength of concrete mixtures
Random forest model to predict the Debye temperature of materials in the AFLOW database
Random forest model to predict the dielectric constant of materials
Random forest model to predict the dilute solute activation energy for diffusion
Random forest model to predict the bandgap of double perovskites
Random forest model to predict the bulk modulus of materials
Random forest model to predict the exfoliation energy of materials
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