Provenance-grouped protocol for evaluating machine learning in perovskite photovoltaics
Standard random data splits in machine learning for perovskite solar cells leak information through massive internal duplication, rewarding simple recall instead of true physical understanding. The authors developed a provenance-grouped protocol to systematically eliminate these leaks by grouping data based on device duplicates, publications, and institutions. This reveals a critical shift where complex neural models outperform simpler ones when tested on genuinely new devices.