New protocol improves ML evaluation for perovskite solar cells

A new protocol for evaluating machine learning models in perovskite photovoltaics has been developed to address the issue of data duplication in existing random splitting methods. This provenance-grouped protocol progressively isolates duplicate devices, publications, and institutions, providing a more rigorous assessment of true physical generalization. Benchmarking thirteen models revealed that while tree ensembles perform well on familiar data, neural architectures excel when evaluating genuinely unseen data and future discoveries, highlighting the importance of device architecture and fabrication methodology alongside absorber chemistry. AI

IMPACT Establishes a more rigorous standard for ML model evaluation in materials science, potentially accelerating discovery by distinguishing true generalization from data memorization.

RANK_REASON The cluster contains an academic paper detailing a new protocol for evaluating machine learning models. [lever_c_demoted from research: ic=1 ai=1.0]

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