2024/04/17 by Yuze Liu, Liu, Yuze, Lejia Wang +3 · 1 voice · 1 citation
Materials Science · Computer Science · Biochemistry, Genetics and Molecular Biology · #Chemical Physics (physics.chem-ph) #Data Analysis #FOS: Physical sciences #J.2 #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.2404.11348
Machine learning model development in chemistry and materials science often grapples with the challenge of small scale, unbalanced labelled datasets, a common limitation in scientific experiments. These dataset imbalances can precipitate overfit ting and diminish model generalization. Our study explores the efficacy of the farthest point sampling (FPS) strategy within target ed chemical feature spaces, demonstrating its capacity to generate well-distributed training datasets and consequently enhance model performance. We rigorously evaluated this strategy across various machine learning models, including artificial neural net works (ANN), support vector machines (SVM), and random forests (RF), using datasets encapsulating physicochemical properties like standard boiling points and enthalpy of vaporization. Our findings reveal that FPS-based models consistently surpass those trained via random sampling, exhibiting superior predictive accuracy and robustness, alongside a marked reduction in overfitting. This improvement is particularly pronounced in smaller training datasets, attributable to increased diversity within the training data's chemical feature space. Consequently, FPS emerges as a universally effective and adaptable approach in approaching high performance machine learning models by small and biased experimental datasets prevalent in chemistry and materials science.