Strain data augmentation enables machine learning of inorganic crystal geometry optimization.

Machine-learning (ML) models offer the potential to rapidly evaluate the vast inorganic crystalline materials space to efficiently find materials with properties that meet the challenges of our time. Current ML models require optimized equilibrium structures to attain accurate predictions of formation energies. However, equilibrium structures are generally not known for new materials and must be obtained through computationally expensive optimization, bottlenecking ML-based material screening.