Symmetry-invariant quantum machine learning force fields
This work addresses the problem of generating accurate force fields for atomistic simulations in chemistry, offering an incremental improvement by integrating symmetry invariance into quantum models.
The authors tackled the challenge of trainability and scalability in quantum machine learning force fields by designing quantum neural networks that incorporate physical symmetries as a data-inspired prior. Their invariant models outperformed generic counterparts on complex molecules, with a water dimer example demonstrating versatility for larger simulations.
Machine learning techniques are essential tools to compute efficient, yet accurate, force fields for atomistic simulations. This approach has recently been extended to incorporate quantum computational methods, making use of variational quantum learning models to predict potential energy surfaces and atomic forces from ab initio training data. However, the trainability and scalability of such models are still limited, due to both theoretical and practical barriers. Inspired by recent developments in geometric classical and quantum machine learning, here we design quantum neural networks that explicitly incorporate, as a data-inspired prior, an extensive set of physically relevant symmetries. We find that our invariant quantum learning models outperform their more generic counterparts on individual molecules of growing complexity. Furthermore, we study a water dimer as a minimal example of a system with multiple components, showcasing the versatility of our proposed approach and opening the way towards larger simulations. Our results suggest that molecular force fields generation can significantly profit from leveraging the framework of geometric quantum machine learning, and that chemical systems represent, in fact, an interesting and rich playground for the development and application of advanced quantum machine learning tools.