Machine learning approaches, including neural network potentials and machine-learning force fields, are capable of performing molecular geometry optimizations with near quantum-mechanical accuracy at a fraction of the computational cost.
The retrieved literature contains multiple recent studies demonstrating the direct application of machine learning techniques to molecular geometry optimization and potential energy surface exploration, fully supporting the claim.