ReNeGate: A Reaction Network Graph-Theoretical Tool for Automated Mechanistic Studies in Computational Homogeneous Catalysis

Holographic Gaussian Boson Sampling with Matrix Product States on 3D cQED Processors

We introduce quantum circuits for simulations of multimode state vectors on 3D circuit quantum electrodynamics (cQED) processors using matrix product state representations. The circuits are demonstrated as applied to simulations of molecular docking based on holographic Gaussian boson sampling (GBS), as illustrated for the binding of a thiol-containing aryl sulfonamide ligand to the tumor necrosis factor-α converting enzyme receptor. We show that cQED devices with a modest number of modes could be employed to simulate multimode systems by repurposing working modes through measurement and reinitialization. We anticipate that a wide range of GBS applications could be implemented on compact 3D cQED processors analogously using the holographic approach. Simulations on qubit-based quantum computers could be implemented analogously using circuits that represent continuous variables in terms of truncated expansions of Fock states.

Paving the road towards automated homogeneous catalyst design

In the past decade, computational tools have become integral to catalyst design. They continue to offer significant support to experimental organic synthesis and catalysis researchers aiming for optimal reaction outcomes. More recently, data-driven approaches utilizing machine learning have garnered considerable attention for their expansive capabilities. This Perspective provides an overview of diverse initiatives in the realm of computational catalyst design and introduces our automated tools tailored for high-throughput in silico exploration of the chemical space. While valuable insights are gained through methods for high-throughput in silico exploration and analysis of chemical space, their degree of automation and modularity are key. We argue that the integration of data-driven, automated and modular workflows is key to enhancing homogeneous catalyst design on an unprecedented scale, contributing to the advancement of catalysis research.

Exploring Chemical Space Using Ab Initio Hyperreactor Dynamics

In recent years, first-principles exploration of chemical reaction space has provided valuable insights into intricate reaction networks. Here, we introduce ab initio hyperreactor dynamics, which enables rapid screening of the accessible chemical space from a given set of initial molecular species, predicting new synthetic routes that can potentially guide subsequent experimental studies. For this purpose, different hyperdynamics derived bias potentials are applied along with pressure-inducing spherical confinement of the molecular system in ab initio molecular dynamics simulations to efficiently enhance reactivity under mild conditions. To showcase the advantages and flexibility of the hyperreactor approach, we present a systematic study of the method's parameters on a HCN toy model and apply it to a recently introduced experimental model for the prebiotic formation of glycinal and acetamide in interstellar ices, which yields results in line with experimental findings. In addition, we show how the developed framework enables the study of complicated transitions like the first step of a nonenzymatic DNA nucleoside synthesis in an aqueous environment, where the molecular fragmentation problem of earlier nanoreactor approaches is avoided.

Time-Resolved Graphs of Polymorphic Cycles for H-Bonded Network Identification in Flexible Biomolecules

A novel approach based on a coarse-grained representation of topological graphs is proposed for the automatic analysis of molecular dynamics (MD) trajectories of hydrogen-bonded (H-Bonded) flexible biomolecules. Herein, our approach models an H-Bonded biomolecule by its H-Bonded cycles and its graph of cycles in which the vertices and links represent the intersections between these cycles. We propose a methodology in which each identified conformer/isomer from the MD is represented by a well-chosen set of H-Bonded cycles called a minimum cycle basis. The key component is the "polycycles" that distinguish the cycles that play the same polymorphic role in the molecule from the ones that lead to an actual conformational change of the molecule. The relevance of our proposed method is evaluated on MD trajectories of gas-phase biomolecules, for which the covalent bonds are unchanged over time and only the hydrogen bonds change over time. The polygraphs and their time evolution are shown to reveal the dynamicity of the metastructure(s) of the H-Bonded biomolecules while providing polymorphic information on the cycles. Such information on the dynamics and changes in the H-bond network, as some cycles change identity while retaining the same role in the overall structure, is not easily captured at the atomic level of representation. Such information can instead be captured by polymorphic cycles.

Automated MUltiscale simulation environment

Multiscale techniques integrating detailed atomistic information on materials and reactions to predict the performance of heterogeneous catalytic full-scale reactors have been suggested but lack seamless implementation. The largest challenges in the multiscale modeling of reactors can be grouped into two main categories: catalytic complexity and the difference between time and length scales of chemical and transport phenomena. Here we introduce the Automated MUltiscale Simulation Environment AMUSE, a workflow that starts from Density Functional Theory (DFT) data, automates the analysis of the reaction networks through graph theory, prepares it for microkinetic modeling, and subsequently integrates the results into a standard open-source Computational Fluid Dynamics (CFD) code. We demonstrate the capabilities of AMUSE by applying it to the unimolecular iso-propanol dehydrogenation reaction and then, increasing the complexity, to the pre-commercial Pd/In2O3 catalyst employed for the CO2 hydrogenation to methanol. The results show that AMUSE allows the computational investigation of heterogeneous catalytic reactions in a comprehensive way, providing essential information for catalyst design from the atomistic to the reactor scale level.

Automated Exploration of Reaction Networks and Mechanisms Based on Metadynamics Nanoreactor Simulations

We developed an automated approach to construct a complex reaction network and explore the reaction mechanisms for numerous reactant molecules by integrating several theoretical approaches. Nanoreactor-type molecular dynamics was used to generate possible chemical reactions, in which the metadynamics was used to overcome the reaction barriers, and the semiempirical GFN2-xTB method was used to reduce the computational cost. Reaction events were identified from trajectories using the hidden Markov model based on the evolution of the molecular connectivity. This provided the starting points for further transition-state searches at the electronic structure levels of density functional theory to obtain the reaction mechanism. Finally, the entire reaction network containing multiple pathways was built. The feasibility and efficiency of the automated construction of the reaction network were investigated using the HCHO and NH3 biomolecular reaction and the reaction network for a multispecies system comprising dozens of HCN and H2O molecules. The results indicated that the proposed approach provides a valuable and effective tool for the automated exploration of the reaction networks.

HiREX: High-Throughput Reactivity Exploration for Extended Databases of Transition-Metal Catalysts

A method is introduced for the automated analysis of reactivity exploration for extended in silico databases of transition-metal catalysts. The proposed workflow is designed to tackle two key challenges for bias-free mechanistic explorations on large databases of catalysts: (1) automated exploration of the chemical space around each catalyst with unique structural and chemical features and (2) automated analysis of the resulting large chemical data sets. To address these challenges, we have extended the application of our previously developed ReNeGate method for bias-free reactivity exploration and implemented an automated analysis procedure to identify the classes of reactivity patterns within specific catalyst groups. Our procedure applied to an extended series of representative Mn(I) pincer complexes revealed correlations between structural and reactive features, pointing to new channels for catalyst transformation under the reaction conditions. Such an automated high-throughput virtual screening of systematically generated hypothetical catalyst data sets opens new opportunities for the design of high-performance catalysts as well as an accelerated method for expert bias-free high-throughput in silico reactivity exploration.

Multi-Time-Scale Simulation of Complex Reactive Mixtures: How Do Polyoxometalates Form?

Understanding the dynamics of reactive mixtures still challenges both experiments and theory. A relevant example can be found in the chemistry of molecular metal-oxide nanoclusters, also known as polyoxometalates. The high number of species potentially involved, the interconnectivity of the reaction network, and the precise control of the pH and concentrations needed in the synthesis of such species make the theoretical/computational treatment of such processes cumbersome. This work addresses this issue relying on a unique combination of recently developed computational methods that tackle the construction, kinetic simulation, and analysis of complex chemical reaction networks. By using the Bell-Evans-Polanyi approximation for estimating activation energies, and an accurate and robust linear scaling for correcting the computed pKa values, we report herein multi-time-scale kinetic simulations for the self-assembly processes of polyoxotungstates that comprise 22 orders of magnitude, from tens of femtoseconds to months of reaction time. This very large time span was required to reproduce very fast processes such as the acid/base equilibria (at 10-12 s), relatively slow reactions such as the formation of key clusters such as the metatungstate (at 103 s), and the very slow assembly of the decatungstate (at 106 s). Analysis of the kinetic data and of the reaction network topology shed light onto the details of the main reaction mechanisms, which explains the origin of kinetic and thermodynamic control followed by the reaction. Simulations at alkaline pH fully reproduce experimental evidence since clusters do not form under those conditions.

Performance of homogeneous catalysts viewed in dynamics

Effective assessment of catalytic performance is the foundation for the rational design and development of new catalysts with superior performance. The ubiquitous screening/optimization studies use reaction yields as the sole performance metric in an approach that often neglects the complexity of the catalytic system and intrinsic reactivities of the catalysts. Using an example of hydrogenation catalysis, we examine the transient behavior of catalysts that are often encountered in activation, deactivation and catalytic turnover processes. Each of these processes and the reaction environment in which they take place are gradually shown to determine the real-time catalyst speciation and the resulting kinetics of the overall catalytic reaction. As a result, the catalyst performance becomes a complex and time-dependent metric defined by multiple descriptors apart from the reaction yield. This behaviour is not limited to hydrogenation catalysis and affects various catalytic transformations. In this feature article, we discuss these catalytically relevant descriptors in an attempt to arrive at a comprehensive depiction of catalytic performance.