About this Abstract |
Meeting |
2024 TMS Annual Meeting & Exhibition
|
Symposium
|
Algorithm Development in Materials Science and Engineering
|
Presentation Title |
Quantum Approximate Bayesian Optimization Algorithm for Design of High-entropy Alloys |
Author(s) |
Jungin E Kim, Yan Wang |
On-Site Speaker (Planned) |
Yan Wang |
Abstract Scope |
Quantum computing is an emerging computational paradigm that can help accelerate the discovery and design of materials. In this work, a quantum approximate Bayesian optimization algorithm is developed to search for the ground state of materials. The new algorithm can efficiently minimize potential energies through two aspects. First, the quantum circuit, which determines discrete variable values, consists of two types of mixers in an alternating fashion. One is a Pauli-gate mixer which encourages exploration by perturbing the quantum system. The other is a generalized Grover mixer which emphasizes exploitation through amplitude amplification. Second, surrogate-based Bayesian optimization is performed where a surrogate model efficiently guides the search process. The new hybrid quantum-classical algorithm is demonstrated with the design of high-entropy alloys, where the atomic structure of CoCrFeMnNi is identified. The locations and numbers of constituent elements, electron configurations, and bond lengths corresponding to the stable states are found. |
Proceedings Inclusion? |
Planned: |
Keywords |
Computational Materials Science & Engineering, ICME, |