uw-cmg

DefectSTEMVideoAnalysis

Defect STEM Video Analysis

MAST

MAterials Simulation Toolkit for use with pymatgen

MAST-ML

MAterials Simulation Toolkit for Machine Learning (MAST-ML)

MAST-SEY

Monte Carlo Modeling of Secondary Electron Emission using fully DFT input

MATLAB-loop-detection

ML_bandgap

MoleProp

Multitype-Defect-Detection

Multitype Defect Detection with Faster R-CNN

perovskite-oxide-stability-prediction

code package with elemental property dictionary that trains a model based on training dataset and gives prediction on new perovskite compounds

StructOpt

uw-cmg/MAST-ML

MAterials Simulation Toolkit for Machine Learning (MAST-ML)

uw-cmg/MoleProp

uw-cmg/materials_application_domain_machine_learning

A package for definining the domain of a machine learning model via a feature dissimilarity metric.

uw-cmg/model_bandgap

Random forest model to predict the electronic bandgap of materials

uw-cmg/model_thermal_conductivity

Random forest model to predict the thermal conductivity of materials

uw-cmg/RPV_model

Ensemble neural network model for predicting transition temperature shifts of RPV alloys

uw-cmg/material_error_bar_predictions

Vidit's work on error bar predictions.

uw-cmg/model_debyeT_aflow

Random forest model to predict the Debye temperature of materials in the AFLOW database

uw-cmg/model_dielectric

Random forest model to predict the dielectric constant of materials

uw-cmg/model_diffusion

Random forest model to predict the dilute solute activation energy for diffusion

uw-cmg/model_exfoliationE

Random forest model to predict the exfoliation energy of materials

uw-cmg/model_Li_conductivity

Random forest model to predict the conductivity of Li in solid electrolytes

uw-cmg/model_Mg_alloy

Random forest model to predict the yield strength of Mg alloys

uw-cmg/model_oxide_vacancy

Random forest model to predict the formation energy of O vacancies in oxides

uw-cmg/model_perovskite_ASR

Random forest model to predict the area specific resistance (ASR) of perovskite oxides

uw-cmg/model_perovskite_Habs

Random forest model to predict the H absorption uptake of perovskites

uw-cmg/model_perovskite_Opband

Random forest model to predict the O 2p-band center of perovskite oxides

uw-cmg/model_perovskite_stability

Random forest model to predict the perovskite stability (as convex hull energy)

uw-cmg/model_perovskite_tec

Random forest model to predict the thermal expansion coefficient of perovskites

uw-cmg/model_perovskite_workfunction

Random forest model to predict the (001) AO-surface work function of perovskites

uw-cmg/model_phonon_freq

Random forest model to predict maximum phonon frequency of materials

uw-cmg/model_piezoelectric

Random forest model to predict max piezoelectric displacement of materials

uw-cmg/model_RPV_TTS

Random forest model to predict the transition temperature shift (TTS) of reactor pressure vessel (RPV) steels

uw-cmg/model_semiconductor_lvls

Random forest model to predict the defect charge state transition levels in semiconductors

uw-cmg/model_steel_yield

Random forest model to predict the yield strength of steel alloys

uw-cmg/model_superconductivity

Random forest model to predict the superconducting critical temperature of materials

uw-cmg/model_thermalcond_aflow

Random forest model to predict the thermal conductivity of materials, trained from AFLOW database

uw-cmg/model_thermalexp_aflow

Random forest model to predict the thermal expansion coefficient of materials, trained from AFLOW database

uw-cmg/multilearn

Pytorch wrapper for multi-task learning

uw-cmg/transfernet

Use pytorch to transfer learn data