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...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 annealingassisted 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 NbMoTaWalloy,we reproduced the Nb depletion andWenrichment 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.展开更多
基金supported by the Quantum Computing Based on Quantum Advantage Challenge Research(grant RS-2023-00255442)through the National Research Foundation of Korea(NRF)funded by the Korean government(Ministry of Science and ICT).This research also used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory,which is supported by the Office of Science of the U.S.Department of Energy under Contract No.DEAC05-00OR22725The authors also would like to thank the Notre Dame Center for Research Computing for supporting all the simulations in this work.Notice:This manuscript has in part been authored by UT-Battelle,LLC under Contract No.DEAC05-00OR22725 with the U.S.Department of EnergyThe United States Government retains and the publisher,by accepting the article for publication,acknowledges that the U.S.Government retains a non-exclusive,paid up,irrevocable,world-wide license to publish or reproduce the published form of the manuscript,or allow others to do so,for U.S.Government 15 purposes.The Department of Energy will provide public access to these results of federally sponsored research in accordance with theDOE Public Access Plan(http://energy.gov/downloads/doe-publicaccess-plan).
文摘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 annealingassisted 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 NbMoTaWalloy,we reproduced the Nb depletion andWenrichment 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.
