First principles reaction discovery: from the Schrodinger equation to experimental prediction for methane pyrolysis

Development of Parallel On-the-Fly Crystal Algorithm for Reaction Discovery in Large and Complex Molecular Systems

The parallel on-the-fly Crystal algorithm is a new, efficient global search algorithm for exploring single-state potential energy surfaces and conical intersection seam spaces of a wide range of molecules. Despite major developments, its application to complex molecular systems, especially in the condensed phase, remains challenging due to the high dimensionality of the configurational space. In this work, we address this challenge and extend its applicability to the reaction discovery of large and complex molecular photoswitches in various molecular environments, including in the condensed phase with explicit solvent molecules. This is achieved by performing an explicit exploration of a comparatively large Crystal configurational subspace, while gradually relaxing the remaining degrees of freedom. The new Crystal algorithm is applied to the reaction discovery of bilirubin and donor-acceptor Stenhouse adducts, a next-generation class of molecular photoswitches, in vacuum and in the aqueous solution. To this end, we designed an automated and systematic workflow for Crystal to discover and characterize new minima and low-energy reaction pathways in these challenging and complex systems. Our findings demonstrate the algorithm's effectiveness in quickly exploring the configuration space and uncovering kinetically accessible products, offering new insights into the intricate chemical reactivities of these molecules and the roles of molecular environments on the reaction pathways. The results underscore the promising potential of parallelized global exploration methods for reaction discovery in biomolecular systems.

AUTOGRAPH: Chemical Reaction Networks in 3D

Understanding and analyzing large-scale reaction networks is a fundamental challenge due to their complexity and size, often beyond human comprehension. In this paper, we introduce AUTOGRAPH, the first web-based tool designed for the interactive three-dimensional (3D) visualization and construction of reaction networks. AUTOGRAPH emphasizes ease of use, allowing users to intuitively build, modify, and explore individual reaction networks in real time. The platform supports a wide range of formats, including CHEMKIN, ensuring compatibility and seamless integration with existing data. Key features of AUTOGRAPH include advanced 3D visualization techniques combined with a fast force-directed algorithm, shortest-path searching, and filtering, facilitating the in-depth exploration of reaction networks. By providing detailed and interactive visualizations, our tool enhances users' ability to comprehend, analyze, and present complex reaction networks, making it a valuable resource for researchers dealing with intricate chemical systems.

Acceleration of Diffusion in Ab Initio Nanoreactor Molecular Dynamics and Application to Hydrogen Sulfide Oxidation

The computational description of chemical reactivity can become extremely complex when multiple different reaction products and intermediates come into play, forming a chemical reaction network. Therefore, computational methods for the automated construction of chemical reaction networks have been developed in the last decades. One of these methods, ab initio nanoreactor molecular dynamics (NMD), is based on external forces enhancing reactivity by e.g., periodically compressing the system and allowing it to relax. However, during the relaxation process, a significant simulation time is required to allow energy to dissipate and molecules to diffuse, making this part of the NMD simulation computationally intensive. This work aims to improve NMD by accelerating the diffusion process in the relaxation phase. We systematically investigate the speedup of reaction discovery gained by diffusion acceleration, leading to a factor of up to 28 in discovery frequency. Diffusion-accelerated nanoreactor molecular dynamics (DA-NMD) is then used to construct a reaction network of hydrogen sulfide oxidation under atmospheric conditions, where reactions are automatically detected by a change in the bond order and bond distance. A reaction network of 108 molecular species and 399 elementary reactions was constructed starting from hydrogen sulfide, hydroxy radicals, and molecular oxygen covering a broad variety of sulfur-oxygen chemistry and oxidation states of the sulfur atom ranging from -II to +VI.

Molecular dynamics-guided reaction discovery reveals endoperoxide-to-alkoxy radical isomerization as key branching point in α-pinene ozonolysis

Secondary organic aerosols (SOAs) significantly impact Earth's climate and human health. Although the oxidation of volatile organic compounds (VOCs) has been recognized as the major contributor to the atmospheric SOA budget, the mechanisms by which this process produces SOA-forming highly oxygenated organic molecules (HOMs) remain unclear. A major challenge is navigating the complex chemical landscape of these transformations, which traditional hypothesis-driven methods fail to thoroughly investigate. Here, we explore the oxidation of α-pinene, a critical atmospheric biogenic VOC, using a novel reaction discovery approach based on molecular dynamics and state-of-the-art enhanced sampling techniques. Our approach successfully identifies all established reaction pathways of α-pinene ozonolysis, as well as discovers multiple novel species and pathways without relying on a priori chemical knowledge. In particular, we unveil a key branching point that leads to the rapid formation of alkoxy radicals, whose high and diverse reactivity help to explain hitherto unexplained oxidation pathways suggested by mass spectral peaks observed in α-pinene ozonolysis experiments. This branching point is likely prevalent across a variety of atmospheric VOCs and could be crucial in establishing the missing link to SOA-forming HOMs.

Reaction Networks Resemble Low-Dimensional Regular Lattices

The computational exploration, manipulation, and design of complex chemical reactions face fundamental challenges related to the high-dimensional nature of potential energy surfaces (PESs) that govern reactivity. Accurately modeling complex reactions is crucial for understanding the chemical processes involved in, for example, organocatalysis, autocatalytic cycles, and one-pot molecular assembly. Our prior research demonstrated that discretizing PESs using heuristics based on bond breaking and bond formation produces a reaction network representation with a low-dimensional structure (metric space). We now find that these stoichiometry-preserving reaction networks possess additional, though approximate, structure and resemble low-dimensional regular lattices with a small amount of random edge rewiring. The heuristics-based discretization thus generates a nonlinear dimensionality reduction by a factor of 10 with an a posteriori error measure (probability of random rewiring). The structure becomes evident through a comparative analysis of CHNO reaction networks of varying stoichiometries against a panel of size-matched generative network models, taking into account their local, metric, and global properties. The generative models include random networks (Erdős-Rényi and bipartite random networks), regular lattices (periodic and nonperiodic), and network models with a tunable level of "randomness" (Watts-Strogatz graphs and regular lattices with random rewiring). The CHNO networks are simultaneously closely matched in all these properties by 3-4-dimensional regular lattices with 10% or less of edges randomly rewired. The effective dimensionality reduction is found to be independent of the system size, stoichiometry, and ruleset, suggesting that search and sampling algorithms for PESs of complex chemical reactions can be effectively leveraged.

Ultrafast studies of elusive chemical reactions in the gas phase

The chemical composition of the interstellar medium and planetary atmospheres is constantly in flux as atoms and molecules collide and interact with high-energy particles such as electrons, protons, and photons. These transformative processes ultimately lead to the coalescence of molecules and eventually the birth of stars. Our understanding of these chemical ecosystems relies on models that synthesize data from gas-phase experiments, providing insights into reaction cross sections. This Review examines efforts to delve into the fundamental bond-forming and bond-breaking dynamics that occur during bimolecular and electron-initiated reactions. These experiments involve clever approaches to establish a time reference and the collision geometry necessary for tracking atomic motion with femtosecond time resolution. Findings from these efforts enhance present models and improve predictions for molecule-molecule and electron-molecule collisions.

Automated Skeleton Network Generation for ReaxFF Molecular Dynamics Simulations of Hydrocarbon Fuel Pyrolysis and Oxidation via a Rate-Based Algorithm

In this study, we present an automated approach of rate-based skeleton network generation for ReaxFF MD simulation (RxMD-SN) for deriving the reaction kinetic mechanism of large hydrocarbon fuels in pyrolysis and oxidation from large-scale ReaxFF MD simulations. The approach contains the statistical calculation of reaction rate constants and the generation of skeleton reaction networks using a rate-based algorithm. The RxMD-SN method takes advantage of reaction flux ranking at a small time interval in terms of temporal reaction rate to extract the core reaction networks, which allows for keeping the rare reaction events that may be dominant in a certain period of the reaction network. The kinetic models derived from ReaxFF MD simulation in CH4 oxidation can reproduce what was obtained in the ReaxFF MD simulation, which demonstrates the capability of RxMD-SN in capturing the global reaction kinetics. An evaluation of reaction rate constants indicates that close kinetic parameters are shared for n-octane oxidation of similar reaction classes, shared oxidation reactions of CH4 against n-heptane, and shared pyrolysis reactions of the RP-3 surrogate fuel against n-heptane. This capability of RxMD-SN is particularly beneficial in meeting the challenges in characterizing the oxidation reaction kinetics of large hydrocarbon molecules. RxMD-SN approach is potentially a general approach in chemical kinetics modeling on the basis of ReaxFF MD simulations.

Uncertainty-Aware First-Principles Exploration of Chemical Reaction Networks

Exploring large chemical reaction networks with automated exploration approaches and accurate quantum chemical methods can require prohibitively large computational resources. Here, we present an automated exploration approach that focuses on the kinetically relevant part of the reaction network by interweaving (i) large-scale exploration of chemical reactions, (ii) identification of kinetically relevant parts of the reaction network through microkinetic modeling, (iii) quantification and propagation of uncertainties, and (iv) reaction network refinement. Such an uncertainty-aware exploration of kinetically relevant parts of a reaction network with automated accuracy improvement has not been demonstrated before in a fully quantum mechanical approach. Uncertainties are identified by local or global sensitivity analysis. The network is refined in a rolling fashion during the exploration. Moreover, the uncertainties are considered during kinetically steering of a rolling reaction network exploration. We demonstrate our approach for Eschenmoser-Claisen rearrangement reactions. The sensitivity analysis identifies that only a small number of reactions and compounds are essential for describing the kinetics reliably, resulting in efficient explorations without sacrificing accuracy and without requiring prior knowledge about the chemistry unfolding.

Machine Learning of Reactive Potentials

In the past two decades, machine learning potentials (MLPs) have driven significant developments in chemical, biological, and material sciences. The construction and training of MLPs enable fast and accurate simulations and analysis of thermodynamic and kinetic properties. This review focuses on the application of MLPs to reaction systems with consideration of bond breaking and formation. We review the development of MLP models, primarily with neural network and kernel-based algorithms, and recent applications of reactive MLPs (RMLPs) to systems at different scales. We show how RMLPs are constructed, how they speed up the calculation of reactive dynamics, and how they facilitate the study of reaction trajectories, reaction rates, free energy calculations, and many other calculations. Different data sampling strategies applied in building RMLPs are also discussed with a focus on how to collect structures for rare events and how to further improve their performance with active learning.

A human-machine interface for automatic exploration of chemical reaction networks

Autonomous reaction network exploration algorithms offer a systematic approach to explore mechanisms of complex chemical processes. However, the resulting reaction networks are so vast that an exploration of all potentially accessible intermediates is computationally too demanding. This renders brute-force explorations unfeasible, while explorations with completely pre-defined intermediates or hard-wired chemical constraints, such as element-specific coordination numbers, are not flexible enough for complex chemical systems. Here, we introduce a STEERING WHEEL to guide an otherwise unbiased automated exploration. The STEERING WHEEL algorithm is intuitive, generally applicable, and enables one to focus on specific regions of an emerging network. It also allows for guiding automated data generation in the context of mechanism exploration, catalyst design, and other chemical optimization challenges. The algorithm is demonstrated for reaction mechanism elucidation of transition metal catalysts. We highlight how to explore catalytic cycles in a systematic and reproducible way. The exploration objectives are fully adjustable, allowing one to harness the STEERING WHEEL for both structure-specific (accurate) calculations as well as for broad high-throughput screening of possible reaction intermediates.

Biomimetic Frustrated Lewis Pair Catalysts for Hydrogenation of CO to Methanol at Low Temperatures

The industrial production of methanol through CO hydrogenation using the Cu/ZnO/Al2O3 catalyst requires harsh conditions, and the development of new catalysts with low operating temperatures is highly desirable. In this study, organic biomimetic FLP catalysts with good tolerance to CO poison are theoretically designed. The base-free catalytic reaction contains the 1,1-addition of CO into a formic acid intermediate and the hydrogenation of the formic acid intermediate into methanol. Low-energy spans (25.6, 22.1, and 20.6 kcal/mol) are achieved, indicating that CO can be hydrogenated into methanol at low temperatures. The new extended aromatization-dearomatization effect involving multiple rings is proposed to effectively facilitate the rate-determining CO 1,1-addition step, and a new CO activation model is proposed for organic catalysts.

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.

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.

Efficient Acceleration of Reaction Discovery in the Ab Initio Nanoreactor: Phenyl Radical Oxidation Chemistry

Over the years, many computational strategies have been employed to elucidate reaction networks. One of these methods is accelerated molecular dynamics, which can circumvent the expense required in dynamics to find all reactants and products (local minima) and transition states (first-order saddle points) on a potential energy surface (PES) by using fictitious forces that promote reaction events. The ab initio nanoreactor uses these accelerating forces to study large chemical reaction networks from first-principles quantum mechanics. In the initial nanoreactor studies, this acceleration was done through a piston periodic compression potential, which pushes molecules together to induce entropically unfavorable bimolecular reactions. However, the piston is not effective for discovering intramolecular and dissociative reactions, such as those integral to the decomposition channels of phenyl radical oxidation. In fact, the choice of accelerating forces dictates not only the rate of reaction discovery but also the types of reactions discovered; thus, it is critical to understand the biases and efficacies of these forces. In this study, we examine forces using metadynamics, attractive potentials, and local thermostats for accelerating reaction discovery. For each force, we construct a separate phenyl radical combustion reaction network using solely that force in discovery trajectories. We elucidate the enthalpic and entropic trends of each accelerating force and highlight their efficiency in reaction discovery. Comparing the nanoreactor-constructed reaction networks with literature renditions of the phenyl radical combustion PES shows that a combination of accelerating forces is best suited for reaction discovery.