Multireference Methods are Realistic and Useful Tools for Modeling Catalysis

Highly correlated systems, in particular those that include transition metals, are ubiquitous in catalysis. The significant static correlation found in such systems is often poorly accounted for using Kohn Sham density functional theory methods, as they are single determinantal in nature. Applications to catalysis of more rigorous and appropriate multiconfigurational methods have been reported in select instances, but their use remains rare. We discuss obstacles that hinder the routine application of multireference (MR) wave function theoretical calculations to catalytic systems and the current state of the art with respect to removing those obstacles

Beyond Density Functional Theory: the Multiconfigurational Approach to Model Heterogeneous Catalysis

Catalytic processes are crucially important for many practical chemical applications. Heterogeneous catalysts are especially appealing because of their high stability and the relative ease with which they may be recovered and reused. Computational modeling can play an important role in the design of more catalytically active materials through the identification of reaction mechanisms and the opportunity to assess hypothetical catalysts in silico prior to experimental verification. Kohn-Sham density functional theory (KS-DFT) is the most used method in computational catalysis because it is affordable and it gives results of reasonable accuracy in many instances. Furthermore, it can be employed in a “black-box” mode that does not require significant a priori knowledge of the system. However, KS-DFT has some limitations: it suffers from self-interaction error (sometime referred to as delocalization error), but a greater concern is that it provides an intrinsically single-reference description of the electronic structure, and this can be especially problematic for modeling catalysis when transition metals are involved. In this perspective, we highlight some noteworthy applications of KS-DFT to heterogeneous computational catalysis, as well as cases where KS-DFT fails accurately to describe electronic structures and intermediate spin states in open-shell transition metal systems. We next provide an introduction to state-of-the-art multiconfigurational (MC; also referred to as multireference (MR)) methods and their advantages and limitations for modeling heterogeneous catalysis. We focus on specific examples to which MC methods have 2 been applied and discuss the challenges associated with these calculations. We conclude by offering our vision for how the community can make further progress in the development of MC methods for application to heterogeneous catalysis

Zeolites at the molecular level : what can be learned from molecular modeling

This review puts the development of molecular modeling methods in the context of their applications to zeolitic active sites. We attempt to highlight the utmost necessity of close cooperation between theory and experiment, resulting both in advances in computational methods and in progress in experimental techniques

Host–guest interactions in framework materials:Insight from modeling

The performance of metal–organic and covalent organic framework materials in sought-after applications—capture, storage, and delivery of gases and molecules, and separation of their mixtures—heavily depends on the host–guest interactions established inside the pores of these materials. Computational modeling provides information about the structures of these host–guest complexes and the strength and nature of the interactions present at a level of detail and precision that is often unobtainable from experiment. In this Review, we summarize the key simulation techniques spanning from molecular dynamics and Monte Carlo methods to correlate ab initio approaches and energy, density, and wavefunction partitioning schemes. We provide illustrative literature examples of their uses in analyzing and designing organic framework hosts. We also describe modern approaches to the high-throughput screening of thousands of existing and hypothetical metal–organic frameworks (MOFs) and covalent organic frameworks (COFs) and emerging machine learning techniques for predicting their properties and performances. Finally, we discuss the key methodological challenges on the path toward computation-driven design and reliable prediction of high-performing MOF and COF adsorbents and catalysts and suggest possible solutions and future directions in this exciting field of computational materials science

Computational Electrochemistry of 3d Transition Metal Complexes

The topic of this thesis is the computational quantum chemical (QC) description of homogeneous first-row (3d) transition metal (TM) electrocatalysis. This branch of chemistry holds great potential for employing Earth-abundant 3d TMs in renewable energy concepts. Therefore, routine predictions for the reactivity of 3d TM electrocatalysts are desirable, but due to numerous challenges, they are only possible to a limited extent. The thesis describes the development, assessment, and application of QC methods with the aim of such predictions. In the first Chapter, an introduction to the QC treatment of 3d TM electrocatalysis is given, followed by a brief overview of the different QC methods in the second Chapter. The computationally most affordable methods are the semiempirical quantum mechanical (SQM) methods, which are the topic of the third Chapter, where the inclusion of spin-polarization in the extended tight-binding Hamiltonian (xTB) is elaborated. The next higher QC level is density functional theory (DFT), which is the topic of Chapter four. Here, the extension of the non-empirical r²SCAN density functional approximation (DFA) to the hybrid level, resulting in the r2SCANh, r2SCAN0, and r2SCAN50 DFAs, is described. At the highest DFT level are the double-hybrids (DHs), which are the subject of Chapter five. Their applicability is extended with the domain-based local pair natural orbital (DLPNO) approximation for second-order Møller–Plesset perturbation theory (MP2). The highest level belongs to the wave function theory (WFT) methods. Their application can face severe difficulties in 3d TM electrocatalysis due to multireference (MR) character, which is the subject of the sixth Chapter. Here, the recognition of MR systems and the calculation of their ionization potentials (IPs) is studied at the highest feasible WFT level. For this purpose, a new benchmark set of electrocatalysts, termed 3dTMV, is compiled, and coupled cluster calculations (CCSD(T)) as well as quantum Monte Carlo (ph-AFQMC) calculations were conducted. Chapter seven deals with the application of SQM and DFT methods for the elucidation of electrocatalytic cycles with three-legged piano-stool iron complexes. An efficient workflow is presented for the calculation of Gibbs free energies yielding a free energy map that is used to propose an initial catalytic cycle. The extension of the free energy map to also include kinetics by transition state theory is shown in Chapter eight. Finally, in the ninth Chapter, the findings of this work are summarized, and their impact on the theoretical description of 3d TM electrocatalysis and 3d TM chemistry in general, are evaluated. Novel QC workflows can benefit from the methods and findings presented in this work and accelerate the discovery of efficient (electro-)catalysts employing Earth-abundant 3d transition metals

Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their simulation, analysis, and data resources. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions.Comment: 60 pages, 14 figures; comments welcom

Quantum-centric supercomputing for materials science: A perspective on challenges and future directions

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Computationally hard tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their resources for simulation, analysis, and data processing. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions

Mono- and Binuclear Non-Heme Iron Chemistry from a Theoretical Perspective

In this minireview, we provide an account of the current state-of-the-art developments in the area of mono- and binuclear non-heme enzymes (NHFe and NHFe2) and the smaller NHFe(2) synthetic models, mostly from a theoretical and computational perspective. The sheer complexity, and at the same time the beauty, of the NHFe(2) world represent a challenge for both experimental as well as theoretical methods. We emphasize that the concerted progress on both theoretical and experimental side is a conditio sine qua non for future understanding, exploration and utilization of the NHFe(2) systems. After briefly discussing the current challenges and advances in the computational methodology, we review the recent spectroscopic and computational studies of NHFe(2) enzymatic and inorganic systems and highlight the correlations between various experimental data (spectroscopic, kinetic, thermodynamic, electrochemical) and computations. Throughout, we attempt to keep in mind the most fascinating and attractive phenomenon in the NHFe(2) chemistry which is the fact that despite the strong oxidative power of many reactive intermediates, the NHFe(2) enzymes perform catalysis with high selectivity. We conclude with our personal viewpoint and hope that further developments in quantum chemistry and especially in the field of multireference wave function methods are needed to have a solid theoretical basis for the NHFe(2) studies, mostly by providing benchmarking and calibration of the computationally efficient and easy-to-use DFT methods