用量子计算机模拟氧气还原反应的铂基催化剂

IF 9.4 1区 材料科学 Q1 CHEMISTRY, PHYSICAL npj Computational Materials Pub Date : 2024-12-19 DOI:10.1038/s41524-024-01460-x
Cono Di Paola, Evgeny Plekhanov, Michal Krompiec, Chandan Kumar, Emanuele Marsili, Fengmin Du, Daniel Weber, Jasper Simon Krauser, Elvira Shishenina, David Muñoz Ramo
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引用次数: 0

摘要

氢已经成为低碳和可持续交通目的的一种有前途的能源。然而,它的应用仍然受到燃料电池中电催化氧还原反应(ORR)转换效率不高的限制。ORR的复杂性质和强电子相关性的存在对使用经典计算机的原子建模提出了挑战。这种情况为实现新型量子计算工作流程开辟了新的途径。在这里,我们提出了一项最先进的研究,结合经典和量子计算方法来研究铂基表面上的ORR。我们的研究首次证明了在h1系列捕获离子量子计算机上实现该工作流程的可行性,并确定了该反应的量子化学建模的挑战。这些结果突出了量子计算机在解决具有强相关电子结构的众所周知的困难系统方面的巨大潜力,并表明铂/钴是在未来应用中展示量子优势的理想候选者。
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Platinum-based catalysts for oxygen reduction reaction simulated with a quantum computer

Hydrogen has emerged as a promising energy source for low-carbon and sustainable mobility purposes. However, its applications are still limited by modest conversion efficiency in the electrocatalytic oxygen reduction reaction (ORR) within fuel cells. The complex nature of the ORR and the presence of strong electronic correlations present challenges to atomistic modelling using classical computers. This scenario opens new avenues for the implementation of novel quantum computing workflows. Here, we present a state-of-the-art study that combines classical and quantum computational approaches to investigate ORR on platinum-based surfaces. Our research demonstrates, for the first time, the feasibility of implementing this workflow on the H1-series trapped-ion quantum computer and identify the challenges of the quantum chemistry modelling of this reaction. The results highlight the great potentiality of quantum computers in solving notoriously difficult systems with strongly correlated electronic structures and suggest platinum/cobalt as ideal candidate for showcasing quantum advantage in future applications.

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来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
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