Large Reaction Networks Research Articles

Chemography-guided analysis of a reaction path network for ethylene hydrogenation with a model Wilkinson's catalyst.

Visualization and analysis of large chemical reaction networks become rather challenging when conventional graph-based approaches are used. As an alternative, we propose to use the chemical cartography ("chemography") approach, describing the data distribution on a 2-dimensional map. Here, the Generative Topographic Mapping (GTM) algorithm - an advanced chemography approach - has been applied to visualize the reaction path network of a simplified Wilkinson's catalyst-catalyzed hydrogenation containing some 105 structures generated with the help of the Artificial Force Induced Reaction (AFIR) method using either Density Functional Theory or Neural Network Potential (NNP) for potential energy surface calculations. Using new atoms permutation invariant 3D descriptors for structure encoding, we've demonstrated that GTM possesses the abilities to cluster structures that share the same 2D representation, to visualize potential energy surface, to provide an insight on the reaction path exploration as a function of time and to compare reaction path networks obtained with different methods of energy assessment.

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.

Predicting hydrogenolysis reaction barriers of large hydrocarbons on metal surfaces using machine learning: Implications for polymer deconstruction

Calculating activation energies of chemical reactions for large reaction networks is computationally demanding. Traditional Brønsted-Evans-Polanyi (BEP) relationships are useful in this regard but are prone to substantial estimation errors. Here, we explore machine learning models to predict activation energies for hydrogenolysis reactions on transition metals, including Ru, Pt, and Rh. The curated dataset includes 380 DFT-calculated activation energies of C-C and C-H scission reactions ranging from C1 to C6 species. The mean absolute error of traditional BEPs is 0.30 eV, and published BEPs on smaller hydrocarbons give even larger errors. In comparison, an XGBoost model achieves a mean absolute error of 0.19 eV for the test set without needing additional DFT-calculated energies. The model selects the homologous series among reactions and the electronegativity and atomic mass of the metal atoms as key features. The model appears transferable across metals. This approach can provide accurate and rapid estimation of activation energies of large reaction networks, such as those in plastics recycling, to accelerate catalyst screening. We showcase the impact of hydrocarbon chain length and branching on the barriers, discuss implications for the depolymerization rate of isotactic polypropylene and low vs. high-density polyethylene, and demonstrate the feasibility of hydrogenolysis catalyst screening.

Inference of complex reaction mechanisms applying model reduction techniques

Both structural (number of species and reactions) and temporal (extremely diverse reaction rates) aspects of complexity are considered when describing large chemical reaction networks. A consistent way to make model reduction is to construct the invariant manifold, which describes the asymptotic system behavior. Preliminary approximations to SIM (Slow Invariant Manifold) are constructed using the model reduction techniques (MRTs): the Quasi-Equilibrium Manifold (QEM), the Spectral Quasi-Equilibrium Manifold (SQEM), and the Intrinsic Low-Dimension Manifold (ILDM). In this paper, the activities of the concerned species and the overall dynamics of the system are examined. Two examples are used to demonstrate the techniques: the Michaelis–Menten mechanism, which is a single reaction mechanism, and a multi-route route reaction mechanism. The behavior of each species on the available route is covered separately. As a result, the reduced invariant solution curve of several approaches is illustrated, along with a comparison of these methods in various graphs. Sensitivity analysis is applied using the SimBiology toolbox in MATLAB to monitor the role of each parameter involved. All the results of model reduction techniques are simulated through MATLAB.

An Efficient Algorithm for Astrochemical Systems Using Stoichiometry Matrices

Astrochemical simulations are a powerful tool for revealing chemical evolution in the interstellar medium. Astrochemical calculations require efficient processing of large matrices for the chemical networks. The large chemical reaction networks often present bottlenecks for computation because of time derivatives of chemical abundances. We propose an efficient algorithm using a stoichiometry matrix approach in which this time-consuming part is expressed as a loop, unlike the algorithm used in previous studies. Since stoichiometry matrices are sparse in general, the performances of simulations with our algorithm depend on which sparse-matrix storage format is used. We conducted a performance comparison experiment using the common storage formats, including the coordinate format, the compressed column storage format, the compressed row storage (CRS) format, and the sliced ELLPACK format. Experimental results showed that the simulations with the CRS format are the most suitable for astrochemical simulations and about a factor of 2 faster than those with the algorithm used in previous studies. In addition, our algorithm significantly reduces not only the computation time but also the compilation time. We also explore the beneficial effects of parallelization and sparse-matrix reordering in these algorithms.

Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model.

Transition state search is key in chemistry for elucidating reaction mechanisms and exploring reaction networks. The search for accurate 3D transition state structures, however, requires numerous computationally intensive quantum chemistry calculations due to the complexity of potential energy surfaces. Here we developed an object-aware SE(3) equivariant diffusion model that satisfies all physical symmetries and constraints for generating sets of structures-reactant, transition state and product-in an elementary reaction. Provided reactant and product, this model generates a transition state structure in seconds instead of hours, which is typically required when performing quantum-chemistry-based optimizations. The generated transition state structures achieve a median of 0.08 Å root mean square deviation compared to the true transition state. With a confidence scoring model for uncertainty quantification, we approach an accuracy required for reaction barrier estimation (2.6 kcal mol-1) by only performing quantum chemistry-based optimizations on 14% of the most challenging reactions. We envision usefulness for our approach in constructing large reaction networks with unknown mechanisms.

Machine Learning-Based Prediction of Activation Energies for Chemical Reactions on Metal Surfaces.

In computational surface catalysis, the calculation of activation energies of chemical reactions is expensive, which, in many cases, limits our ability to understand complex reaction networks. Here, we present a universal, machine learning-based approach for the prediction of activation energies for reactions of C-, O-, and H-containing molecules on transition metal surfaces. We rely on generalized Bronsted-Evans-Polanyi relationships in combination with machine learning-based multiparameter regression techniques to train our model for reactions included in the University of Arizona Reaction database. In our best approach, we find a mean absolute error for activation energies within our test set of 0.14 eV if the reaction energy is known and 0.19 eV if the reaction energy is unknown. We expect that this methodology will often replace the explicit calculation of activation energies within surface catalysis when exploring large reaction networks or screening catalysts for desirable properties in the future.

The regime-conversion method: a hybrid technique for simulating well-mixed chemical reaction networks

There exist several methods for simulating biological and physical systems as represented by chemical reaction networks. Systems with low numbers of particles are frequently modeled as discrete-state Markov jump processes and are typically simulated via a stochastic simulation algorithm (SSA). An SSA, while accurate, is often unsuitable for systems with large numbers of individuals, and can become prohibitively expensive with increasing reaction frequency. Large systems are often modeled deterministically using ordinary differential equations, sacrificing accuracy and stochasticity for computational efficiency and analytical tractability. In this paper, we present a novel hybrid technique for the accurate and efficient simulation of large chemical reaction networks. This technique, which we name the regime-conversion method, couples a discrete-state Markov jump process to a system of ordinary differential equations by simulating a reaction network using both techniques simultaneously. Individual molecules in the network are represented by exactly one regime at any given time, and may switch their governing regime depending on particle density. In this manner, we model high copy-number species using the cheaper continuum method and low copy-number species using the more expensive, discrete-state stochastic method to preserve the impact of stochastic fluctuations at low copy number. The motivation, as with similar methods, is to retain the advantages while mitigating the shortfalls of each method. We demonstrate the performance and accuracy of our method for several test problems that exhibit varying degrees of inter-connectivity and complexity by comparing averaged trajectories obtained from both our method and from exact stochastic simulation.

Barrier Height Prediction by Machine Learning Correction of Semiempirical Calculations.

Different machine learning (ML) models are proposed in the present work to predict density functional theory-quality barrier heights (BHs) from semiempirical quantum mechanical (SQM) calculations. The ML models include a multitask deep neural network, gradient-boosted trees by means of the XGBoost interface, and Gaussian process regression. The obtained mean absolute errors are similar to those of previous models considering the same number of data points. The ML corrections proposed in this paper could be useful for rapid screening of the large reaction networks that appear in combustion chemistry or in astrochemistry. Finally, our results show that 70% of the features with the highest impact on model output are bespoke predictors. This custom-made set of predictors could be employed by future Δ-ML models to improve the quantitative prediction of other reaction properties.

Generating Fast Specialized Simulators for Stochastic Reaction Networks via Partial Evaluation

Domain-specific modeling languages allow a clear separation between simulation model and simulator and, thus, facilitate the development of simulation models and add to the credibility of simulation results. Partial evaluation provides an effective means for efficiently executing models defined in such languages. However, it also implies some challenges of its own. We illustrate this and solutions based on a simple domain-specific language for biochemical reaction networks as well as on the network representation of the established BioNetGen language. We implement different approaches adopting the same simulation algorithms: one generic simulator that parses models at runtime and one generator that produces a simulator specialized to a given model based on partial evaluation and code generation. For the purpose of better understanding, we additionally generate intermediate variants, where only some parts are partially evaluated. Akin to profile-guided optimization, we use dynamic execution of the model to further optimize the simulators. The performance of the approaches is carefully benchmarked using representative models of small to large biochemical reaction networks. The generic simulator achieves a performance similar to state-of-the-art simulators in the domain, whereas the specialized simulator outperforms established simulation tools with a speedup of more than an order of magnitude. Technical limitations in regard to the size of the generated code are discussed and overcome using a combination of link-time optimization and code separation. A detailed performance study is undertaken, investigating how and where partial evaluation has the largest effect.

Statistical degree screening method for combustion mechanism reduction

From a statistical perspective, large complex reaction networks have the power-law degree distributions, which means a small fraction of hub nodes are responsible for most of the network function, while the remaining large amount of low degree nodes give negligible contributions. Based on this, a statistical degree screening (SDS) combustion mechanism reduction method was proposed and verified in 42 popular mechanisms of different size and developed by various institutes. It is confirmed that most large combustion networks obey the power-law form degree distribution with limited fitting error. Then a reduced model could be obtained by setting a degree threshold according to the degree distribution and prediction error requirements to distinguish the important species to keep. Intensive validation results in the comparison with the directed relation graph (DRG) method-reduced mechanisms prove that despite the SDS method requires considerably less input information and processing time, it can come up with significantly reduced models with comparable or even better prediction ability over a broad parameter range. The good reduction application results of SDS shown here indicates a brand-new angle for large combustion mechanism analysis and reduction, i.e., from the statistical network structure properties aspect. It is of practical significance for the swift analysis and reduction of large combustion mechanisms and can considerably avoid the influence coming from the dynamical parameters that introduce great uncertainty.

Emergence of homochirality in large molecular systems

The selection of a single molecular handedness, or homochirality across all living matter, is a mystery in the origin of life. Frank's seminal model showed in the '50s how chiral symmetry breaking can occur in nonequilibrium chemical networks. However, an important shortcoming in this classic model is that it considers a small number of species, while there is no reason for the prebiotic system, in which homochirality first appeared, to have had such a simple composition. Furthermore, this model does not provide information on what could have been the size of the molecules involved in this homochiral prebiotic system. Here, we show that large molecular systems are likely to undergo a phase transition toward a homochiral state, as a consequence of the fact that they contain a large number of chiral species. Using chemoinformatics tools, we quantify how abundant chiral species are in the chemical universe of all possible molecules of a given length. Then, we propose that Frank's model should be extended to include a large number of species, in order to possess the transition toward homochirality, as confirmed by numerical simulations. Finally, using random matrix theory, we prove that large nonequilibrium reaction networks possess a generic and robust phase transition toward a homochiral state.

Perspective on computational reaction prediction using machine learning methods in heterogeneous catalysis.

Heterogeneous catalysis plays a significant role in the modern chemical industry. Towards the rational design of novel catalysts, understanding reactions over surfaces is the most essential aspect. Typical industrial catalytic processes such as syngas conversion and methane utilisation can generate a large reaction network comprising thousands of intermediates and reaction pairs. This complexity not only arises from the permutation of transformations between species but also from the extra reaction channels offered by distinct surface sites. Despite the success in investigating surface reactions at the atomic scale, the huge computational expense of ab initio methods hinders the exploration of such complicated reaction networks. With the proliferation of catalysis studies, machine learning as an emerging tool can take advantage of the accumulated reaction data to emulate the output of ab initio methods towards swift reaction prediction. Here, we briefly summarise the conventional workflow of reaction prediction, including reaction network generation, ab initio thermodynamics and microkinetic modelling. An overview of the frequently used regression models in machine learning is presented. As a promising alternative to full ab initio calculations, machine learning interatomic potentials are highlighted. Furthermore, we survey applications assisted by these methods for accelerating reaction prediction, exploring reaction networks, and computational catalyst design. Finally, we envisage future directions in computationally investigating reactions and implementing machine learning algorithms in heterogeneous catalysis.

Direct Optimization across Computer-Generated Reaction Networks Balances Materials Use and Feasibility of Synthesis Plans for Molecule Libraries

The synthesis of thousands of candidate compounds in drug discovery and development offers opportunities for computer-aided synthesis planning to simplify the synthesis of molecule libraries by leveraging common starting materials and reaction conditions. We develop an optimization-based method to analyze large organic chemical reaction networks and design overlapping synthesis plans for entire molecule libraries so as to minimize the overall number of unique chemical compounds needed as either starting materials or reaction conditions. We consider multiple objectives, including the number of starting materials, the number of catalysts/solvents/reagents, and the likelihood of success of the overall syntheses plan, to select an optimal reaction network to access the target molecules. The library synthesis planning task was formulated as a network flow optimization problem, and we design an efficient decomposition scheme that reduces solution time by a factor of 5 and scales to instance with 48 target molecules and nearly 8000 intermediate reactions within hours. In four case studies of pharmaceutical compounds, the approach reduces the number of starting materials and catalysts/solvents/reagents needed by 32.2 and 66.0% on average and up to 63.2 and 80.0% in the best cases. The code implementation can be found at https://github.com/Coughy1991/Molecule_library_synthesis.

Identification of strategic molecules for future circular supply chains using large reaction networks

Networks of chemical reactions represent relationships between molecules within chemical supply chains and promise to enhance planning of multi-step synthesis routes from bio-renewable feedstocks. This study aims to identify strategic molecules in chemical reaction networks that may potentially play a significant role within the future circular economy. We mine Reaxys® database in order to assemble a network of chemical reactions. We describe molecules within the network by a portfolio of graph theoretical features, and identify strategic molecules with an isolation forest search algorithm. In this work we have identified a list of potential strategic molecules and indicated possibilities for reaction planning using these. This is exemplified by a potential supply chain of functional molecules from bio-waste streams that could be used as feedstocks without being converted to syngas. This work extends the methodology of analysis of reaction networks to the generic problem of development of new reaction pathways based on novel feedstocks.

Can microsolvation effects be estimated from vacuum computations? A case-study of alcohol decomposition at the H2O/Pt(111) interface.

Converting biomass into sustainable chemicals and energy feed-stocks requires innovative heterogeneous catalysts, which are able to efficiently work under aqueous conditions. Computational chemistry is a key asset in the design of these novel catalysts, but it has to face two challenges: the large reaction networks and the potential role of hydration. They can be addressed using scaling relations such as Brønsted-Evans-Polanyi (BEP) and solvation models, respectively. In this study, we show that typical reaction and activation energies of alcohol decomposition on Pt(111) are not strongly modified by the inclusion of the water solvent as a continuum model. In contrast, adding a single water molecule strongly favours O-H and C-OH scission and it prevents C-O and to a lesser extent C-C scissions. The resulting BEP relationships partially reflect these changes induced by the solvent. Predicting Pt-catalysed alcohol decomposition in water should thus account for the influence of the solvent on thermodynamics and kinetics. In addition, we found that the reaction energies obtained in the presence of an explicit water molecule scale with the ones obtained in a vacuum. Hence, we reveal that vacuum computations in combination with corrections based on our linear regressions are able to capture the important H-bonding effect.

Identification of strategic molecules for future circular supply chains using large reaction networks

Networks of chemical reactions represent relationships between molecules within chemical supply chains and promise to enhance planning of multi-step synthesis routes from bio-renewable feedstocks.

CRISPR/Cas9-Mediated Homology-Directed Genome Editing in Pichia pastoris.

State-of-the-art strain engineering techniques for themethylotrophic yeast Pichia pastoris (syn. Komagataella spp.) include overexpression of endogenous and heterologous genes and deletion of host genes. For efficient gene deletion, methods such as the split-marker technique have been established. However, synthetic biology trends move toward building up large and complex reaction networks, which often require endogenous gene knockouts and simultaneous overexpression of individual genes or whole pathways. Realization of such engineering tasks by conventional approaches employing subsequent steps of transformations and marker recycling is very time- and labor-consuming. Other applications require tagging of certain genes/proteins or promoter exchange approaches, which are hard to design and construct with conventional methods. Therefore, efficient systems are required that allow precise manipulations of the P. pastoris genome, including simultaneous overexpression of multiple genes. To meet this challenge, we have developed a CRISPR/Cas9-based kit for gene insertions, deletions, and replacements, which paves the way for precise genomic modifications in P. pastoris. In this chapter, the versatile method for performing these modifications without the integration of aselection marker is described. A ready-to-use plasmid kit for performing CRISPR/Cas9-mediated genome editing in P. pastoris based on the GoldenPiCS modular cloning vectors is available at Addgene as CRISPi kit (#1000000136).

Parameter sensitivity analysis for biochemical reaction networks.

Biochemical reaction networks describe the chemical interactions occurring between molecular populations inside the living cell. These networks can be very noisy and complex and they often involve many variables and even more parameters. Parameter sensitivity analysis that studies the effects of parameter changes to the behaviour of biochemical networks can be a powerful tool in unravelling their key parameters and interactions. It can also be very useful in designing experiments that study these networks and in addressing parameter identifiability issues. This article develops a general methodology for analysing the sensitivity of probability distributions of stochastic processes describing the time-evolution of biochemical reaction networks to changes in their parameter values. We derive the coefficients that efficiently summarise the sensitivity of the probability distribution of the network to each parameter and discuss their properties. The methodology is scalable to large and complex stochastic reaction networks involving many parameters and can be applied to oscillatory networks. We use the two-dimensional Brusselator system as an illustrative example and apply our approach to the analysis of the Drosophila circadian clock. We investigate the impact of using stochastic over deterministic models and provide an analysis that can support key decisions for experimental design, such as the choice of variables and time-points to be observed.

Developments in the Atomistic Modelling of Catalytic Processes for the Production of Platform Chemicals from Biomass

AbstractThe transformation of biomass‐derived molecules into platform chemicals that can directly be employed by the chemical industry is one of the challenges in catalysis in this century. While some processes are cost‐effective and industrially available, the molecular insight has been advancing at a slow pace when compared to other areas (like energy conversion). In the present review we describe the main challenges imposed on the theoretical simulations for the study of these complex molecules and how the most crucial issues are typically addressed. In particular, we focus on technical aspects like the need for London dispersion and solvation contributions. We also deal with the complexity of reaction networks, which requires new approaches and ways to compile the results in the form of databases. This allows the study of large reaction networks for the decomposition of C2 alcohols, or the plethora of functional groups of cyclic molecules such as lignin and sugars. Finally, we put forward a few applications that show the potential of atomistic simulations in the field.