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.

Contact-Map-Driven Exploration of Heterogeneous Protein-Folding Paths

We have recently shown how physically realizable protein-folding pathways can be generated using directed walks in the space of inter-residue contact-maps; combined with a back-transformation to move from protein contact-maps to Cartesian coordinates, we have demonstrated how this approach can generate protein-folding trajectory ensembles without recourse to molecular dynamics. In this article, we demonstrate that this framework can be used to study a challenging protein-folding problem that is known to exhibit two different folding paths which were previously identified through molecular dynamics simulation at several different temperatures. From the viewpoint of protein-folding mechanism prediction, this particular problem is extremely challenging to address, specifically involving folding to an identical nontrivial compact native structure along distinct pathways defined by heterogeneous secondary structural elements. Here, we show how our previously reported contact-map-based protein-folding strategy can be significantly enhanced to enable accurate and robust prediction of heterogeneous folding paths by (i) introducing a novel topologically informed metric for comparing two protein contact maps, (ii) reformulating our graph-represented folding path generation, and (iii) introducing a new and more reliable structural back-mapping algorithm. These changes improve the reliability of generating structurally sound folding intermediates and dramatically decrease the number of physically irrelevant folding intermediates generated by our previous simulation strategy. Most importantly, we demonstrate how our enhanced folding algorithm can successfully identify the alternative folding mechanisms of a multifolding-pathway protein, in line with direct molecular dynamics simulations.

Predicting Long-Time-Scale Kinetics under Variable Experimental Conditions with Kinetica.jl

Predicting the degradation processes of molecules over long time scales is a key aspect of industrial materials design. However, it is made computationally challenging by the need to construct large networks of chemical reactions that are relevant to the experimental conditions that kinetic models must mirror, with every reaction requiring accurate kinetic data. Here, we showcase Kinetica.jl, a new software package for constructing large-scale chemical reaction networks in a fully automated fashion by exploring chemical reaction space with a kinetics-driven algorithm; coupled to efficient machine-learning models of activation energies for sampled elementary reactions, we show how this approach readily enables generation and kinetic characterization of networks containing ∼103 chemical species and ≃104-105 reactions. Symbolic-numeric modeling of the generated reaction networks is used to allow for flexible, efficient computation of kinetic profiles under experimentally realizable conditions such as continuously variable temperature regimes, enabling direct connection between bottom-up reaction networks and experimental observations. Highly efficient propagation of long-time-scale kinetic profiles is required for automated reaction network refinement and is enabled here by a new discrete kinetic approximation. The resulting Kinetica.jl simulation package therefore enables automated generation, characterization, and long-time-scale modeling of complex chemical reaction systems. We demonstrate this for hydrocarbon pyrolysis simulated over time scales of seconds, using transient temperature profiles representing those of tubular flow reactor experiments.

Enhancing the photocatalytic upcycling of polystyrene to benzoic acid: a combined computational-experimental approach for acridinium catalyst design

Converting polystyrene into value-added oxygenated aromatic compounds is an attractive end-of-life upcycling strategy. However, identification of appropriate catalysts often involves laborious and time-consuming empirical screening. Herein, after demonstrating the feasibility of using acridinium salts for upcycling polystyrene into benzoic acid by photoredox catalysis for the first time, we applied low-cost descriptor-based combinatorial in silico screening to predict the photocatalytic performance of a family of potential candidates. Through this approach, we identified a non-intuitive fluorinated acridinium catalyst that outperforms other candidates for converting polystyrene to benzoic acid in useful yields at low catalyst loadings (≤5 mol%). In addition, this catalyst also proved effective with real-life polystyrene waste containing dyes and additives. Our study underscores the potential of computer-aided catalyst design for valorizing polymeric waste into essential chemical feedstock for a more sustainable future.

A Reactive Molecular Dynamics Study of Chlorinated Organic Compounds. Part II: A ChemTraYzer Study of Chlorinated Dibenzofuran Formation and Decomposition Processes

In our two-paper series, we first present the development of ReaxFF CHOCl parameters using the recently published ParAMS parametrization tool. In this second part, we update the reactive Molecular Dynamics - Quantum Mechanics coupling scheme ChemTraYzer and combine it with our new ReaxFF parameters from Part I to study formation and decomposition processes of chlorinated dibenzofurans. We introduce a self-learning method for recovering failed transition-state searches that improves the overall ChemTraYzer transition-state search success rate by 10 percentage points to a total of 48 %. With ChemTraYzer, we automatically find and quantify more than 500 reactions using transition state theory and DFT. Among the discovered chlorinated dibenzofuran reactions are numerous reactions that are new to the literature. In three case studies, we discuss the set of reactions that are most relevant to the dibenzofuran literature: (i) bimolecular reactions of the chlorinated-dibenzofuran precursors phenoxy radical and 1,3,5-trichlorobenzene, (ii) dibenzofuran chlorination and pyrolysis, and (iii) oxidation of chlorinated dibenzofurans.

Graph-Driven Reaction Discovery: Progress, Challenges, and Future Opportunities

Graph-based descriptors, such as bond-order matrices and adjacency matrices, offer a simple and compact way of categorizing molecular structures; furthermore, such descriptors can be readily used to catalog chemical reactions (i.e., bond-making and -breaking). As such, a number of graph-based methodologies have been developed with the goal of automating the process of generating chemical reaction network models describing the possible mechanistic chemistry in a given set of reactant species. Here, we outline the evolution of these graph-based reaction discovery schemes, with particular emphasis on more recent methods incorporating graph-based methods with semiempirical and ab initio electronic structure calculations, minimum-energy path refinements, and transition state searches. Using representative examples from homogeneous catalysis and interstellar chemistry, we highlight how these schemes increasingly act as "virtual reaction vessels" for interrogating mechanistic questions. Finally, we highlight where challenges remain, including issues of chemical accuracy and calculation speeds, as well as the inherent challenge of dealing with the vast size of accessible chemical reaction space.

Successes and challenges in using machine-learned activation energies in kinetic simulations

The prediction of the thermodynamic and kinetic properties of chemical reactions is increasingly being addressed by machine-learning (ML) methods such as artificial neural networks (ANNs). While a number of recent studies have reported success in predicting chemical reaction activation energies, less attention has focused on how the accuracy of ML predictions filter through to predictions of macroscopic observables. Here, we consider the impact of the uncertainty associated with ML prediction of activation energies on observable properties of chemical reaction networks, as given by microkinetics simulations based on ML-predicted reaction rates. After training an ANN to predict activation energies given standard molecular descriptors for reactants and products alone, we performed microkinetics simulations of three different prototypical reaction networks: formamide decomposition, aldol reactions and decomposition of 3-hydroperoxypropanal. We find that the kinetic modelling predictions can be in excellent agreement with corresponding simulations performed with ab initio calculations, but this is dependent on the inherent energetic landscape of the networks. We use these simulations to suggest some guidelines for when ML-based activation energies can be reliable, and when one should take more care in applications to kinetics modelling.

Fast and automated identification of reactions with low barriers using meta-MD simulations

We test our meta-molecular dynamics (MD) based approach for finding low-barrier (<30 kcal/mol) reactions on uni- and bimolecular reactions extracted from the barrier dataset developed by Grambow, Pattanaik & Green (2020). For unimolecular reactions the meta-MD simulations identify 25 of the 26 products found by Grambow, Pattanaik & Green (2020), while the subsequent semiempirical screening eliminates an additional four reactions due to an overestimation of the reaction energies or estimated barrier heights relative to DFT. In addition, our approach identifies 36 reactions not found by Grambow, Pattanaik & Green (2020), 10 of which are <30 kcal/mol. For bimolecular reactions the meta-MD simulations identify 19 of the 20 reactions found by Grambow, Pattanaik & Green (2020), while the subsequent semiempirical screening eliminates an additional reaction. In addition, we find 34 new low-barrier reactions. For bimolecular reactions we found that it is necessary to “encourage” the reactants to go to previously undiscovered products, by including products found by other MD simulations when computing the biasing potential as well as decreasing the size of the molecular cavity in which the MD occurs, until a reaction is observed. We also show that our methodology can find the correct products for two reactions that are more representative of those encountered in synthetic organic chemistry. The meta-MD hyperparameters used in this study thus appear to be generally applicable to finding low-barrier reactions.

Efficient Reaction Space Exploration with ChemTraYzer-TAD

The development of a reaction model is often a time-consuming process, especially if unknown reactions have to be found and quantified. To alleviate the reaction modeling process, automated procedures for reaction space exploration are highly desired. We present ChemTraYzer-TAD, a new reactive molecular dynamics acceleration technique aimed at efficient reaction space exploration. The new method is based on the basin confinement strategy known from the temperature-accelerated dynamics (TAD) acceleration method. Our method features integrated ChemTraYzer bond-order processing steps for the automatic and on-the-fly determination of the positions of virtual walls in configuration space that confine the system in a potential energy basin. We use the example of 1,3-dioxolane-4-hydroperoxide-2-yl radical oxidation to show that ChemTraYzer-TAD finds more than 100 different parallel reactions for the given set of reactants in less than 2 ns of simulation time. Among the many observed reactions, ChemTraYzer-TAD finds the expected typical low-temperature reactions despite the use of extremely high simulation temperatures up to 5000 K. Our method also finds a new concerted β-scission plus O₂ addition with a lower reaction barrier than the literature-known and so-far dominant β-scission.

New Tools for Taming Complex Reaction Networks: The Unimolecular Decomposition of Indole Revisited

The level of detail attained in the computational description of reaction mechanisms can be vastly improved through tools for automated chemical space exploration, particularly for systems of small to medium size. Under this approach, the unimolecular decomposition landscape for indole was explored through the automated reaction mechanism discovery program AutoMeKin. Nevertheless, the sheer complexity of the obtained mechanisms might be a hindrance regarding their chemical interpretation. In this spirit, the new Python library amk-tools has been designed to read and manipulate complex reaction networks, greatly simplifying their overall analysis. The package provides interactive dashboards featuring visualizations of the network, the three-dimensional (3D) molecular structures and vibrational normal modes of all chemical species, and the corresponding energy profiles for selected pathways. The combination of the joined mechanism generation and postprocessing workflow with the rich chemistry of indole decomposition enabled us to find new details of the reaction (obtained at the CCSD(T)/aug-cc-pVTZ//M06-2X/MG3S level of theory) that were not reported before: (i) 16 pathways leading to the formation of HCN and NH₃ (via amino radical); (ii) a barrierless reaction between methylene radical and phenyl isocyanide, which might be an operative mechanism under the conditions of the interstellar medium; and (iii) reaction channels leading to both hydrogen cyanide and hydrogen isocyanide, of potential astrochemical interest as the computed HNC/HCN ratios greatly exceed the calculated equilibrium value at very low temperatures. The reported reaction networks can be very valuable to supplement databases of kinetic data, which is of remarkable interest for pyrolysis and astrochemical studies.

Assessing and rationalizing the performance of Hessian update schemes for reaction path Hamiltonian rate calculations

The reaction path Hamiltonian (RPH) can be used to calculate chemical reaction rate constants, going beyond transition-state theory in taking account of recrossing by providing an approximation to the dynamic transmission coefficient. However, the RPH necessitates the calculation of the Hessian matrix at a number of points along the minimum energy path; the associated computational cost stands as a bottleneck in RPH calculations, especially if one is interested in using high-accuracy electronic structure methods. In this work, four different Hessian update schemes (symmetric rank-1, Powell-symmetric Broyden, Bofill, and TS-BFGS updates) are assessed to see whether or not they reliably reproduce calculated transmission coefficients for three different chemical reactions. Based on the reactions investigated, the symmetric rank-1 Hessian update was the least appropriate for RPH construction, giving different transmission coefficients from the standard analytical Hessian approach, as well as inconsistent frequencies and coupling properties. The Bofill scheme, the Powell-symmetric Broyden scheme, and the TS-BFGS scheme were the most reliable Hessian update methods, with transmission coefficients that were in good agreement with those calculated by the standard RPH calculations. The relative accuracy of the different Hessian update schemes is further rationalized by investigating the approximated Coriolis and curvature coupling terms along the reaction-path, providing insight into when these schemes would be expected to work well. Furthermore, the associated computational cost associated with the RPH calculations was substantially reduced by the tested update schemes. Together, these results provide useful rules-of-thumb for using Hessian update schemes in RPH simulations.

Exploring human-guided strategies for reaction network exploration: Interactive molecular dynamics in virtual reality as a tool for citizen scientists

The emerging fields of citizen science and gamification reformulate scientific problems as games or puzzles to be solved. Through engaging the wider non-scientific community, significant breakthroughs may be made by analyzing citizen-gathered data. In parallel, recent advances in virtual reality (VR) technology are increasingly being used within a scientific context and the burgeoning field of interactive molecular dynamics in VR (iMD-VR) allows users to interact with dynamical chemistry simulations in real time. Here, we demonstrate the utility of iMD-VR as a medium for gamification of chemistry research tasks. An iMD-VR "game" was designed to encourage users to explore the reactivity of a particular chemical system, and a cohort of 18 participants was recruited to playtest this game as part of a user study. The reaction game encouraged users to experiment with making chemical reactions between a propyne molecule and an OH radical, and "molecular snapshots" from each game session were then compiled and used to map out reaction pathways. The reaction network generated by users was compared to existing literature networks demonstrating that users in VR capture almost all the important reaction pathways. Further comparisons between humans and an algorithmic method for guiding molecular dynamics show that through using citizen science to explore these kinds of chemical problems, new approaches and strategies start to emerge.

AutoMeKin2021: An open-source program for automated reaction discovery

AutoMeKin2021 is an updated version of tsscds2018, a program for the automated discovery of reaction mechanisms (J. Comput. Chem. 2018, 39, 1922). This release features a number of new capabilities: rare-event molecular dynamics simulations to enhance reaction discovery, extension of the original search algorithm to study van der Waals complexes, use of chemical knowledge, a new search algorithm based on bond-order time series analysis, statistics of the chemical reaction networks, a web application to submit jobs, and other features. The source code, manual, installation instructions and the website link are available at: https://rxnkin.usc.es/index.php/AutoMeKin.

Trends in computational molecular catalyst design

Computational methods have emerged as a powerful tool to augment traditional experimental molecular catalyst design by providing useful predictions of catalyst performance and decreasing the time needed for catalyst screening. In this perspective, we discuss three approaches for computational molecular catalyst design: (i) the reaction mechanism-based approach that calculates all relevant elementary steps, finds the rate and selectivity determining steps, and ultimately makes predictions on catalyst performance based on kinetic analysis, (ii) the descriptor-based approach where physical/chemical considerations are used to find molecular properties as predictors of catalyst performance, and (iii) the data-driven approach where statistical analysis as well as machine learning (ML) methods are used to obtain relationships between available data/features and catalyst performance. Following an introduction to these approaches, we cover their strengths and weaknesses and highlight some recent key applications. Furthermore, we present an outlook on how the currently applied approaches may evolve in the near future by addressing how recent developments in building automated computational workflows and implementing advanced ML models hold promise for reducing human workload, eliminating human bias, and speeding up computational catalyst design at the same time. Finally, we provide our viewpoint on how some of the challenges associated with the up-and-coming approaches driven by automation and ML may be resolved.

ChemDyME: Kinetically Steered, Automated Mechanism Generation through Combined Molecular Dynamics and Master Equation Calculations

In many scientific fields, there is an interest in understanding the way in which chemical networks evolve. The chemical networks which researchers focus upon have become increasingly complex, and this has motivated the development of automated methods for exploring chemical reactivity or conformational change in a "black-box" manner, harnessing modern computing resources to automate mechanism discovery. In this work, we present a new approach to automated mechanism generation which couples molecular dynamics and statistical rate theory to automatically find kinetically important reactions and then solve the time evolution of the species in the evolving network. The key to this chemical network mapping through combined dynamics and ME simulation approach is the concept of "kinetic convergence", whereby the search for new reactions is constrained to those species which are kinetically favorable at the conditions of interest. We demonstrate the capability of the new approach for two systems, a well-studied combustion system and a multiple oxygen addition system relevant to atmospheric aerosol formation.

A chemically consistent graph architecture for massive reaction networks applied to solid-electrolyte interphase formation

Modeling reactivity with chemical reaction networks could yield fundamental mechanistic understanding that would expedite the development of processes and technologies for energy storage, medicine, catalysis, and more. Thus far, reaction networks have been limited in size by chemically inconsistent graph representations of multi-reactant reactions (e.g. A + B → C) that cannot enforce stoichiometric constraints, precluding the use of optimized shortest-path algorithms. Here, we report a chemically consistent graph architecture that overcomes these limitations using a novel multi-reactant representation and iterative cost-solving procedure. Our approach enables the identification of all low-cost pathways to desired products in massive reaction networks containing reactions of any stoichiometry, allowing for the investigation of vastly more complex systems than previously possible. Leveraging our architecture, we construct the first ever electrochemical reaction network from first-principles thermodynamic calculations to describe the formation of the Li-ion solid electrolyte interphase (SEI), which is critical for passivation of the negative electrode. Using this network comprised of nearly 6000 species and 4.5 million reactions, we interrogate the formation of a key SEI component, lithium ethylene dicarbonate. We automatically identify previously proposed mechanisms as well as multiple novel pathways containing counter-intuitive reactions that have not, to our knowledge, been reported in the literature. We envision that our framework and data-driven methodology will facilitate efforts to engineer the composition-related properties of the SEI - or of any complex chemical process - through selective control of reactivity.

Simple position and orientation preconditioning scheme for minimum energy path calculations

Minimum-energy path (MEP) calculations, such as those typified by the nudged elastic band method, require input of reactant and product molecular configurations at initialization. In the case of reactions involving more than one molecule, generating initial reactant and product configurations requires careful consideration of the relative position and orientations of the reactive molecules in order to ensure that the resulting MEP calculation proceeds without converging on an alternative reaction-path, and without requiring excessive numbers of optimization iterations; as such, this initial system set-up is most commonly performed "by hand," with an expert user arranging reactive molecules in space to ensure that the following MEP calculation runs smoothly. In this Article, we introduce a simple preconditioning scheme which replaces this labor-intensive, human-knowledge-based step with an automated deterministic computational scheme. In our approach, initial reactant and product configurations are generated such that steric hindrance between reactive molecules is minimized in the reactant and product configurations, while also simultaneously requiring minimal structural differences between the reactants and products. The method is demonstrated using a benchmark test-set of >3400 organic molecular reactions, where comparison of the reactant/product configurations generated using our approach compare very well to initial configurations which were generated on an ad hoc basis.

Organic thermally activated delayed fluorescence (TADF) compounds used in photocatalysis

Organic compounds that show Thermally Activated Delayed Fluorescence (TADF) have become wildly popular as next-generation emitters in organic light emitting diodes (OLEDs). Since 2016, a subset of these have found increasing use as photocatalysts. This review comprehensively highlights their potential by documenting the diversity of the reactions where an organic TADF photocatalyst can be used in lieu of a noble metal complex photocatalyst. Beyond the small number of TADF photocatalysts that have been used to date, the analysis conducted within this review reveals the wider potential of organic donor-acceptor TADF compounds as photocatalysts. A discussion of the benefits of compounds showing TADF for photocatalysis is presented, which paints a picture of a very promising future for organic photocatalyst development.