Learning the Limits: How Data, Diversity, and Representation Control Machine-Learning Predictions of Reorganisation Energy

Malin Zollner; Yashar Moshfeghi; Tahereh Nematiaram

doi:10.1039/d5tc04408a

January 1, 2026Journal of Materials Chemistry COpen Access

Learning the Limits: How Data, Diversity, and Representation Control Machine-Learning Predictions of Reorganisation Energy

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Key Points

The aim is to determine how data diversity and representation influence the predictions of hole and electron reorganisation energies in organic semiconductors.
Analyzed data sets of organic semiconductor properties
Utilized machine learning algorithms to predict reorganisation energies
Assessed the impact of data diversity on prediction accuracy
Demonstrated that increased data diversity improves predictive accuracy
Found significant variations in predictions based on representation of training data
Observed that machine learning outperforms traditional calculation methods under certain conditions

Implication

This research uncovers the role of data and representation in predicting reorganisation energy in organic semiconductors, suggesting important implications for material design.

Abstract

Accurate and scalable prediction of hole and electron reorganisation energies (λ_h and λ_e ) is a persistent bottleneck in the data-driven design of organic semiconductors, as routine ab initio calculations...

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Cite This Study

Zollner et al. (2026) studied this question.

synapsesocial.com/papers/698d6e4a5be6419ac0d53d5d https://doi.org/10.1039/d5tc04408a

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Learning the Limits: How Data, Diversity, and Representation Control Machine-Learning Predictions of Reorganisation Energy

Key Points

Implication

Abstract

Discussion

Cite This Study

Learning the Limits: How Data, Diversity, and Representation Control Machine-Learning Predictions of Reorganisation Energy

Key Points

Implication

Abstract

Discussion

Cite This Study

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