Quantum annealing-assisted lattice optimization

Quantum annealing-assisted lattice optimization

Blog Article

Abstract High Entropy Alloys (HEAs) have drawn great interest due to their exceptional properties compared to conventional materials.The configuration of white feather tinsel HEA system is considered a key to their superior properties, but exhausting all possible configurations of atom coordinates and species to find the ground energy state is extremely challenging.In this work, we proposed a quantum annealing-assisted lattice optimization (QALO) algorithm, which is an active learning framework that integrates the Field-aware Factorization Machine (FFM) as the surrogate model for lattice energy prediction, Quantum Annealing (QA) as an optimizer and Machine Learning Potential (MLP) for ground truth energy calculation.By applying our algorithm to the NbMoTaW incra clamps alloy, we reproduced the Nb depletion and W enrichment observed in bulk HEA.

We found our optimized HEAs to have superior mechanical properties compared to the randomly generated alloy configurations.Our algorithm highlights the potential of quantum computing in materials design and discovery, laying a foundation for further exploring and optimizing structure-property relationships.