ml-materials-discovery

Accelerate material discovery with machine learning. In this project I use a very small dataset of electrolyte compositions and their lab measured properties to build a model that aims at understanding the relationship between composistion and property. Then I develop a generative model to identify new compositions that maximize the desired property. These compositions are sent to the lab for anlaysis and the process is repeteated. This enables a faster discovery of new materials as opposed to the traditional trial and error method.

Project Organization

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├── data/               <- The original, immutable data dump. 
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├── figures/            <- Figures saved by scripts or notebooks.
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├── src/      <- Python module with source code of this project.
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├── environment.yml     <- conda virtual environment definition file.
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├── Materials Discovery.ipynb     <- Jupyter Notebook that contains the exploratory data analysis, modeling, and new material generation.
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├── LICENSE
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└── README.md           <- The top-level README for developers using this project.

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Project based on the cookiecutter data science project template.

Set up

Install the virtual environment with conda and activate it:

$ conda env create -f environment.yml
$ conda activate example-project 

Install src in the virtual environment:

$ pip install --editable .