Data-Efficient Machine learning for Predicting Dopant Formation Energies in TiO$_2$ Monolayer

TL;DR

This study demonstrates that physically grounded, compact datasets enable accurate machine learning predictions of dopant formation energies in TiO₂ monolayers, even with limited training data.

Contribution

It introduces a descriptor-based machine learning approach that achieves accurate, transferable predictions of dopant energies using small, physically relevant datasets.

Findings

Accurate predictions with limited data are possible using physically relevant descriptors.

Model performance remains robust across different dopant types.

Adding more data improves accuracy and transferability.

Abstract

Machine learning models are increasingly applied in materials science, yet their predictive power is often constrained by data scarcity. Here, we show that accurate predictions can be achieved, even with a limited number of training examples, provided the dataset is compact and and grounded in physically relevant quantities. By combining density functional theory calculations with a machine-learning framework, we construct accurate descriptor-based models to predict the formation energies of doped lepidocrocite TiO$_2$ monolayers. The predictive accuracy of machine-learning models was first evaluated for single-dopant Pt configurations, demonstrating that the selected structural and chemical descriptors reliably capture the key factors governing dopant stability. Chemical transferability is then examined by extending the dataset to include Ag-doped configurations. Predictive accuracy improved systematically as additional Ag-doped data points were included in the training, while the performance of Pt remains robust. These results highlight the potential of small and well-curated datasets combined with physically informed descriptors to enable not only accurate but also chemically transferable machine-learning-driven screening in doped TiO$_2$ monolayer.