ArticleACS materials Au2026
Data-Efficient Machine Learning for Predicting Dopant Formation Energies in TiO
Article in ACS materials Au, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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4 authors.
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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 that the dataset is compact and grounded in physically relevant quantities. By combining density functional theory calculations with a machine learning framework, we constructed accurate descriptor-based models to predict the formation energies of doped lepidocrocite TiO
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