Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery
Traditional machine learning (ML) metrics overestimate model performance for materials discovery. We introduce (1) leave-one-cluster-out cross-validation (LOCO CV) and (2) a simple nearest-neighbor benchmark to show that model performance in discovery applications strongly depends on the problem, data sampling, and extrapolation. Our results suggest that ML-guided iterative experimentation may outperform standard high-throughput screening for discovering breakthrough materials like high-Tc superconductors with ML.
- This article is part of the themed collections: MSDE most-read Q1 2019 and Machine Learning and Data Science in Materials Design