Periodic Density Matrix Embedding for CO Adsorption on the MgO(001) Surface

Abstract

The adsorption of simple gas molecules to metal oxide surfaces is a primary step in many heterogeneous catalysis applications. Quantum chemical modeling of these reactions is a challenge in terms of both cost and accuracy, and quantum-embedding methods are promising, especially for localized chemical phenomena. In this work, we employ density matrix embedding theory (DMET) for periodic systems to calculate the adsorption energy of CO to the MgO(001) surface. Using coupled-cluster theory with single and double excitations and second-order Møller–Plesset perturbation theory as quantum chemical solvers, we perform calculations with embedding clusters up to 266 electrons in 306 orbitals, with the largest embedding models agreeing to within 1.2 kcal/mol of the non-embedding references. Moreover, we present a memory-efficient procedure of storing and manipulating electron repulsion integrals in the embedding space within the framework of periodic DMET.

Ab initio Quantum Simulation of Strongly Correlated Materials with Quantum Embedding

“…Although, the importance of correlation potential fitting in DMET calculations for the electronic structure of magnetic NiO is highlighted by the improved results obtained with multi-fragment embedding, it's worth noting that the impact of self-consistency in DMET calculations can vary depending on the system and the flavor of DMET used. For instance, previous studies have reported minimal impact on single-embedding calculations of molecules [76] and Gamma-point solid-state systems [77,78], while improved results have been observed with multi-fragment embedding [45,47,48]. Second, we find that the orbital-based partition scheme is overall in excellent agreement with the unit cell embedding despite employing much smaller fragments.…”

Recent innovations in the technology and applications of low-dimensional CuO nanostructures for sensing, energy and catalysis

“…1–4 This is because the very existence of designed (intra-material and material–environment) interfaces with specific properties makes HMMs superior to simpler, often single-component, materials. The list of actual applications of HMMs is enormous, with the most relevant ones being biotechnology, 5,6 sensors 7–9 and biosensors, 10,11 energy harvesting systems, 12,13 nanomedicine, 14,15 photocatalysis 16 and chemical catalysis, 17,18 hydrogen energy, 19 water purification, 20 environmental protection 21 and space exploration. 22–25…”

“…[1][2][3][4] This is because the very existence of designed (intra-material and material-environment) interfaces with specific properties makes HMMs superior to simpler, often single-component, materials. The list of actual applications of HMMs is enormous, with the most relevant ones being biotechnology, 5,6 sensors [7][8][9] and biosensors, 10,11 energy harvesting systems, 12,13 nanomedicine, 14,15 photocatalysis 16 and chemical catalysis, 17,18 hydrogen energy, 19 water purification, 20 environmental protection 21 and space exploration. [22][23][24][25] Metal-oxide nanomaterials Over the past twenty years, researchers have investigated metaloxide nanomaterials extensively, showing their adaptivity to different length scales and presenting competitive morphological properties for various applications (Fig.…”

Efficient Multiconfigurational Quantum Chemistry Approach to Single-Ion Magnets Based on Density Matrix Embedding Theory

“…In the meanwhile there are continuous efforts in developing various quantum embedding methods − that combine low-level and high-level quantum solvers to treat different parts of a system in a consistent manner, which are especially effective for systems with spatially isolated strongly correlated centers. Among large varieties of embedding methods, density matrix embedding theory − (DMET), pioneered by Chan and co-workers as a simplified alternative to dynamical mean-field theory (DMFT), , is particularly promising as a general quantum embedding framework and has been successfully applied in various model and realistic systems in recent years. − ,− As suggested by its intimate connection to the density matrix renormalization group (DMRG) formalism manifested through the Schmidt decomposition procedure, DMET appropriately captures the essential entanglement between the cluster of interest and its environment, which would be naively left out by less sophisticated embedding schemes such as ab initio model potential (AIMP) , and density-based embedding…”

“…Recently, inspiring progress on marrying the multiconfigurational solvers with DMET have been made by Gagliardi and co-workers in the exploration of the DMET+CAS technique for strongly correlated systems. − ,,, Their works have clearly shown that besides the compatibility with coupled-cluster (CC) and DMRG high-level solvers, DMET also performs very well with CASSCF and further NEVPT2 solvers and provided promising results on both bond dissociation curves and excitation energies, mainly for systems with main group elements. The success of the DMET framework has thus heightened interest in revealing its potential utility in more intricate systems with, for example, spin–orbit coupling (SOC) effects and more localized and correlated d/f-electrons.…”