Quantum annealing-assisted lattice optimization

Xu, Zhihao; Shang, Wenjie; Kim, Seongmin; Lee, Eungkyu; Luo, Tengfei

doi:10.1038/s41524-024-01505-1

Quantum annealing-assisted lattice optimization

Journal Article · Tue Jan 07 00:00:00 EST 2025 · npj Computational Materials

DOI:https://doi.org/10.1038/s41524-024-01505-1· OSTI ID:2496809

^[1]; Shang, Wenjie ^[1]; ^[2]; ^[3]; ^[1]

University of Notre Dame, IN (United States)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). National Center for Computational Sciences (NCCS)
Kyung Hee Univ., Yongin-si, Gyeonggi-do (Korea, Republic of)

High Entropy Alloys (HEAs) have drawn great interest due to their exceptional properties compared to conventional materials. The configuration of 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 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.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC); National Research Foundation of Korea (NRF); USDOE

Grant/Contract Number:: AC05-00OR22725; RS-2023-00255442

OSTI ID:: 2496809

Alternate ID(s):: OSTI ID: 2498454

Journal Information:: npj Computational Materials, Vol. 11, Issue 1; ISSN 2057-3960

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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