Non-Linear Machine Learning for Enhanced Predictions in Low-Data Chemistry

TL;DR;

This study explores the use of non-linear machine learning algorithms in scenarios with limited data, common in computational chemistry. It introduces automated workflows that incorporate random forests, gradient boosting, and neural networks, optimized using a Bayesian hyperparameter protocol. The research demonstrates that these non-linear models can rival or surpass linear regression in predictive accuracy and maintain interpretability, expanding analytical capabilities without sacrificing robustness.