Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery. Issue 5 (17th August 2018) - Meredig, Bryce; Antono, Erin; Church, Carena; Hutchinson, Maxwell; Ling, Julia; Paradiso, Sean; Blaiszik, Ben; Foster, Ian; Gibbons, Brenna; Hattrick-Simpers, Jason; Mehta, Apurva; Ward, Logan
Abstract : Traditional machine learning (ML) metrics overestimate model performance for materials discovery. Abstract : 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- T c superconductors with ML.