ml-weight-fraction

This project uses machine learning (Python programming language) to approximate weight fractions of emerging chemicals in consumer products.

Table of Contents

• Launching the Project
• Core Components
• Status
• Contact

Launching the project

Before cloning this repository, try launching it through your browser:

Through the Binder interface, users may access all of our code in interactive iPython notebooks self-contained in a virtual, executable environment. I.e., the code for this project is entirely reproducible simply by clicking the button above.

Suggested Software

If you prefer to clone our Git repository, we suggest these software versions:

• Python 3.7.3
• Poetry 1.0 or higher

After cloning, launch the project by running the following in your command line interface:

cd ml-weight-fraction
poetry install
poetry run jupyter lab

Core Components

The suggested order to read/execute iPython notebooks:

• functions.ipynb: Contains functions used across multiple notebooks (optional with Binder).
• modelpipeline.ipynb: Framework for data-poor scenarios that tests and optimizes multiple machine learning prediction algorithms; includes (optional) augmentation of nanomaterials product data with organics product data.
• organicspipeline.ipynb: Framework for data-rich scenarios that tests and optimizes multiple machine learning prediction algorithms.

Post-processing data files are provided. If you wish to go through the pre-processing steps and generate data summaries, run these notebooks:

• preprocessENM.ipynb: Preprocessing steps for the nanomaterials product dataset (data-poor).
• preprocessorganics.ipynb: Preprocessing steps for the bulk-scale organic chemical product dataset (data-rich).

Acronyms and Abbreviations

• arr = array
• bp = boiling point
• cv = cross validation
• df = data frame
• enm = engineered nanomaterials
• matrix_F = matrix of the product is a formulation (i.e., not a solid)
• ml = machine learning
• mp = melting point
• mw = molecular weight
• oecd = Organisation for Economic Co-operation and Development
• prop = property
• puc = product use category
• rbf = radial basis function (a non-linear implementation of SVM)
• rfc = random forest classifier
• svc = support vector classifier (for categorical data)
• svm = support vector machine
• wf = weight fraction

Status

This project has been submitted for publication. Data sources are provided in the article.

Contact

Luka L. Thornton (thorn.luka@gmail.com)