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Prioritizing Data Quality in Machine Learning for Thermophysical Property Prediction: A Case Study on Normal Boiling

Frank T Mtetwa1, Neil F Giles1, W Vincent Wilding1

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High-quality data is crucial for accurate machine learning (ML) predictions of normal boiling points (NBP). Rigorously curated experimental data yields better ML models than larger, uncurated datasets for chemical process design.

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Area of Science:

  • Chemical Engineering
  • Computational Chemistry
  • Machine Learning

Background:

  • Accurate normal boiling point (NBP) prediction is vital for chemical process design and optimization.
  • Traditional methods like group contribution (GC) and quantitative structure-property relationship (QSPR) have limitations in flexibility and accuracy.
  • Machine learning (ML) offers promise but faces challenges in ease of use and reliability for compounds outside training sets, often due to data quality issues.

Purpose of the Study:

  • To investigate the impact of data quality on ML model performance for NBP prediction.
  • To compare ML models trained on curated versus uncurated datasets.
  • To develop a user-friendly tool for accessible NBP prediction.

Main Methods:

  • Trained ML models using LightGBM and PyTorch.
  • Evaluated four molecular featurization methods: RDKit descriptors, Joback groups, and two graph neural network (GNN) representations.
  • Assessed model performance using 10-fold cross-validation on a curated experimental dataset (DIPPR 801) and a larger, uncurated dataset.
  • Developed a web application for easy access to the best-performing model.

Main Results:

  • ML models trained on the smaller, rigorously curated dataset consistently outperformed those trained on the larger, uncurated dataset.
  • The curated dataset approach demonstrated superior accuracy, reduced bias, and better generalization capabilities.
  • Data quality, not just quantity, was identified as the critical factor for reliable ML-driven chemical property prediction.

Conclusions:

  • A data-centric approach, prioritizing data quality, is essential for developing robust ML models in chemical property prediction.
  • The findings advocate for the use of rigorously curated experimental data for training ML models.
  • A practical web application is provided to enable nonspecialists to predict NBP using simple SMILES formulas.