دورية أكاديمية

How Well Can Quantum Embedding Method Predict the Reaction Profiles for Hydrogenation of Small Li Clusters?

التفاصيل البيبلوغرافية
العنوان: How Well Can Quantum Embedding Method Predict the Reaction Profiles for Hydrogenation of Small Li Clusters?
المؤلفون: Dominic Alfonso, Benjamin Avramidis, Hari P. Paudel, Yuhua Duan
المصدر: Nanomaterials, Vol 14, Iss 15, p 1267 (2024)
بيانات النشر: MDPI AG, 2024.
سنة النشر: 2024
المجموعة: LCC:Chemistry
مصطلحات موضوعية: quantum computing, quantum simulator, active space embedding methods, full configuration interaction, coupled cluster methods, hydrogenation reactions, Chemistry, QD1-999
الوصف: Quantum computing leverages the principles of quantum mechanics in novel ways to tackle complex chemistry problems that cannot be accurately addressed using traditional quantum chemistry methods. However, the high computational cost and available number of physical qubits with high fidelity limit its application to small chemical systems. This work employed a quantum-classical framework which features a quantum active space-embedding approach to perform simulations of chemical reactions that require up to 14 qubits. This framework was applied to prototypical example metal hydrogenation reactions: the coupling between hydrogen and Li2, Li3, and Li4 clusters. Particular attention was paid to the computation of barriers and reaction energies. The predicted reaction profiles compare well with advanced classical quantum chemistry methods, demonstrating the potential of the quantum embedding algorithm to map out reaction profiles of realistic gas-phase chemical reactions to ascertain qualitative energetic trends. Additionally, the predicted potential energy curves provide a benchmark to compare against both current and future quantum embedding approaches.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2079-4991
Relation: https://www.mdpi.com/2079-4991/14/15/1267; https://doaj.org/toc/2079-4991
DOI: 10.3390/nano14151267
URL الوصول: https://doaj.org/article/40a6aba03bc0428098a972672c96f9b2
رقم الأكسشن: edsdoj.40a6aba03bc0428098a972672c96f9b2
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20794991
DOI:10.3390/nano14151267