mldice 0.1.6

Machine Learned Diffusion Coefficient Estimator - ML-DiCE is an ML framework that can predict five modes of elemental diffusion in alloys

ML-DiCE | Machine Learned Diffusion Coefficient Estimator

Installation

pip install mldice

Usage

Create a conda environment as

conda create --name myEnv

Activate the environment created

conda activate myEnv

In the activated environment, run

mldice -options

ML-DiCE will featurize your alloy or impure metal and predict diffusion coefficient in m^2/s. The options can be set through following arguments

Use -de to specify diffusing element -dm to specify diffusion medium. Examples:

• -de Fe would take iron as diffusing element.
• -dm Ni75.5Cu24Co0.5 select Ni75.5Cu24Co0.5 is the diffusion medium where constituent elements expressed as percentage
• -t 500 would select the temperature (say, 500K in this case) of diffusion process in Kelvin.
• -m self select self diffusion mechanism. Mechanisms include self, impurity and chemical modes.
• -e RF would select Random Forest regression as prediction algorithm. DNN selects neural network based prediction.

Essentially, the run command shall be as follows:

ML-DiCE.py -de [diffusing element] -dm [diffusion medium] -t [temperature] -s [prediction algorithm] -e RF

Output

All outputs can be found in the Prediction.md file. It contains the following information:

Predicted parameters

Property	Value
Predicted D	-- m^2/s
RMSE	-- m^2/s
MAE	-- m^2/s
Uncertainty	-- m^2/s

Online Ressources

• https://arjun.skv.net/SI (Supporting Information)
• https://arjunskv.net/main (Main article)

Citation

@software{mldice,
  author       = {Kulathuvayal Arjun S., Su Yanqing },
  title        = {{Elemental Diffusion Coefficient Prediction in Conventional Alloys using Machine Learning}},
  month        = june,
  year         = 2024,
  publisher    = {--},
  version      = {v0.0.1},
  doi          = {--},
  url          = {--}
}

Under development

Advanced featurization for alloys: New featurization schemes are under developing