Ab initio structure prediction accelerated with machine learning interatomic potentials

Prescreening candidate structures with inexpensive classical potentials is gaining renewed interest due to recent advances in machine learning (ML) modeling methodology. For effective acceleration of ab initio prediction, ML interatomic potentials must provide accurate description of diverse configurations probed in global structure searches. I will overview an automated framework implemented in our MAISE package that constructs practical neural network interatomic models for multi-element chemical systems. Recent applications of the approach to well-studied crystalline and nanoscale materials have led to the prediction of stable structures overlooked in previous investigations. Our studies highlight evident advantages and remaining limitations of the ML methodology in guiding ab initio structure prediction.