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. A computationally efficient structure optimizer is therefore highly desirable. In this work, we present an ML model capable of predicting the crystal energy response to global strain by using available elasticity data to augment the dataset. The addition of global strains improves our model's understanding of local strains too, significantly improving the accuracy of energy predictions on distorted structures. This allows us to construct an ML-based geometry optimizer, which we used for improving the predictions of formation energy for structures with perturbed atomic positions.