ArticleChemistry of materials : a publication of the American Chemical Society2025
Band-Gap Regression with Architecture-Optimized Message-Passing Neural Networks.
Article in Chemistry of materials : a publication of the American Chemical Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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Who cites it
2 citing papers in PubMed.
- An exciting Approach to Theoretical Spectroscopy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Band-Gap Regression with Architecture-Optimized Message-Passing Neural Networks.Chemistry of materials : a publication of the American Chemical Society · 2025Article
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Authors and funding
4 authors.
Funding
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Abstract
Graph-based neural networks and, specifically, message-passing neural networks (MPNNs) have shown great potential in predicting physical properties of solids. In this work, we train an MPNN to first classify materials through density functional theory data from the AFLOW database as being metallic or semiconducting/insulating. We then perform a neural-architecture search to explore the model architecture and hyperparameter space of MPNNs to predict the band gaps of the materials identified as nonmetals. The top-performing models from the search are pooled into an ensemble that significantly outperforms the best single model. Uncertainty quantification is evaluated with Monte Carlo dropout and ensembling, with the ensemble method proving superior. The domain of applicability of the ensemble model is analyzed with respect to the crystal systems, the inclusion of a Hubbard parameter in the density-functional-theory calculations, and the atomic species building up the materials.
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Registered trials
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