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.

Global Neural Network Potential with Explicit Many-Body Functions for Improved Descriptions of Complex Potential Energy Surface

The high dimensional machine learning potential (MLP) that has developed rapidly in the past decade represents a giant step forward in large-scale atomic simulation for complex systems. The long-range interaction and the poor description of chemical reactions are typical problems of high dimensional MLP, which are mainly caused by the poor structure discrimination of the atom-centered ML model. Herein, we propose a low-cost neural-network-based MLP architecture for fitting global potential energy surface data, namely, G-MBNN, that can offer improved energy and force resolution on a complex potential energy surface. In G-MBNN, a set of many-body energy terms based on the local atomic environment are explicitly included in computing the total energy─the total energy of the system is written as the sum of atomic energy and many-body energy contributions. These extra many-body energy terms are computationally low-cost and, importantly, can provide easy access to delicate energy terms in complex systems such as very short repulsion, long-range attractions, and sensitive angular-dependent covalent interactions. We implement G-MBNN in the LASP code and demonstrate the improved accuracy of the new framework in representative systems, including ternary-element energy materials LiCoOx, TiO2 with defects, and a series of organic reactions.

Combined molecular dynamics and coordinate driving method for automatically searching complicated reaction pathways

The combined molecular dynamics and coordinate driving (MD/CD) method is updated and generalized in this work to broaden its applications in automatically searching reaction pathways for complicated reactions. In this updated version, MD simulations are performed with the GFN's family of methods to systematically sample conformers for almost any systems with atomic numbers Z ≤ 86. The improved CD procedure is greatly accelerated by applying a pre-screening stage at the semiempirical GFN2-xTB level. An automatic module based on the Marcus theory and its improved version (the Wolynes theory) is designed to include single electron transfer (SET) processes into reaction pathways. The capabilities of this method are demonstrated by exploring the most possible reaction pathways of three experimentally reported reactions: the organophosphine-catalyzed trans phosphinoboration, the Fe(II) complex-mediated C(sp2)-H borylation reaction, and the SET-triggered deaminative radical cross-coupling reaction. Comprehensive reaction networks are obtained for all three reactions with reasonable computational costs. Detailed mechanisms for these reactions can account for the reported experimental facts.

Prediction of High-Yielding Single-Step or Cascade Pericyclic Reactions for the Synthesis of Complex Synthetic Targets

Pericyclic reactions, which involve cyclic concerted transition states without ionic or radical intermediates, have been extensively studied since their definition in the 1960s, and the famous Woodward-Hoffmann rules predict their stereoselectivity and chemoselectivity. Here, we describe the application of a fully automated reaction-path search method, that is, the artificial force induced reaction (AFIR), to trace an input compound back to reasonable starting materials through thermally allowed pericyclic reactions via product-based quantum-chemistry-aided retrosynthetic analysis (QCaRA) without using any a priori experimental knowledge. All categories of pericyclic reactions, including cycloadditions, ene reactions, group-transfer, cheletropic, electrocyclic, and sigmatropic reactions, were successfully traced back via concerted reaction pathways, and starting materials were computationally obtained with the correct stereochemistry. Furthermore, AFIR was used to predict whether the identified reaction pathway can be expected to occur in good yield relative to other possible reactions of the identified starting material. In order to showcase its practical utility, this state-of-the-art technology was also applied to the retrosynthetic analysis of a natural product with a relatively high number of atoms (52 atoms: endiandric acid C methyl ester), which was first synthesized by Nicolaou in 1982 and provided the corresponding starting polyenes with the correct stereospecificity via three pericyclic reaction cascades (one Diels-Alder reaction as well as 6π and 8π electrocyclic reactions). Moreover, not only systems that obey the Woodward-Hoffmann rules but also systems that violate these rules, such as those recently calculated by Houk, can be retrosynthesized accurately.

Artificial intelligence pathway search to resolve catalytic glycerol hydrogenolysis selectivity

The complex interaction between molecules and catalyst surfaces leads to great difficulties in understanding and predicting the activity and selectivity in heterogeneous catalysis. Here we develop an end-to-end artificial intelligence framework for the activity prediction of heterogeneous catalytic systems (AI-Cat method), which takes simple inputs from names of molecules and metal catalysts and outputs the reaction energy profile from the input molecule to low energy pathway products. The AI-Cat method combines two neural network models, one for predicting reaction patterns and the other for providing the reaction barrier and energy, with a Monte Carlo tree search to resolve the low energy pathways in a reaction network. We then apply AI-Cat to resolve the reaction network of glycerol hydrogenolysis on Cu surfaces, which is a typical selective C-O bond activation system and of key significance for biomass-derived polyol utilization. We show that glycerol hydrogenolysis features a huge reaction network of relevant candidates, containing 420 reaction intermediates and 2467 elementary reactions. Among them, the surface-mediated enol-keto tautomeric resonance is a key step to facilitate the primary C-OH bond breaking and thus selects 1,2-propanediol as the major product on Cu catalysts. 1,3-Propanediol can only be produced under strong acidic conditions and high surface H coverage by following a hydrogenation-dehydration pathway. AI-Cat further discovers six low-energy reaction patterns for C-O bond activation on metals that is of general significance to polyol catalysis. Our results demonstrate that the reaction prediction for complex heterogeneous catalysis is now feasible with AI-based atomic simulation and a Monte Carlo tree search.

Zeolite-confined subnanometric PtSn mimicking mortise-and-tenon joinery for catalytic propane dehydrogenation

Heterogeneous catalysts are often composite materials synthesized via several steps of chemical transformation, and thus the atomic structure in composite is a black-box. Herein with machine-learning-based atomic simulation we explore millions of structures for MFI zeolite encapsulated PtSn catalyst, demonstrating that the machine-learning enhanced large-scale potential energy surface scan offers a unique route to connect the thermodynamics and kinetics within catalysts' preparation procedure. The functionalities of the two stages in catalyst preparation are now clarified, namely, the oxidative clustering and the reductive transformation, which form separated Sn₄O₄ and PtSn alloy clusters in MFI. These confined clusters have high thermal stability at the intersection voids of MFI because of the formation of "Mortise-and-tenon Joinery". Among, the PtSn clusters with high Pt:Sn ratios (>1:1) are active for propane dehydrogenation to propene, ∼10³ in turnover-of-frequency greater than conventional Pt₃Sn metal. Key recipes to optimize zeolite-confined metal catalysts are predicted.

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.

High-pressure phases of a Mn-N system

Highly compressed extended states of light elemental solids have emerged recently as a novel group of energetic materials. The application of these materials is seriously limited by the energy-safety contradiction, because the material with high energy density is highly metastable and can hardly be recovered under ambient conditions. Recently, it has been found that high-energy density transition metal polynitrides could be synthesized at ∼100 GPa and recovered at ∼20 GPa. Inspired by these findings, we have studied a high-pressure Mn-N system from the aspects of structure, stability, phase transition, energy density and electronic structure theoretically for the first time. The results reveal that MnN₄_P1̄ consisting of [N₄]_∞^2- is thermodynamically stable at 36.9-100 GPa, dynamically stable at 0 GPa and has a noticeably high volumetric energy density of 15.71 kJ cm^-3. Upon decompression, this structure will transform to MnN₄_C2/m with the transition barrier declining sharply at 5-10 GPa due to the switching of transition pathways. Hence, we propose MnN₄_P1̄ as a potential energetic material that is synthesizable above 40 GPa and recoverable until 10 GPa.

Hydrogen Coupling on Platinum Using Artificial Neural Network Potentials and DFT

To date, the understanding of reactions at solid-liquid interfaces has proven challenging, mainly because of the inaccessible nature of such systems to current experimental techniques with atomic resolution. This has meant that many important features, including free energy barriers and the atomistic structure of intermediates, remain unknown. To tackle these issues, we construct and utilize a high-dimensional neural network (HDNN) potential for the simulation of hydrogen evolution at the HCl(aq)/Pt(111) interface, taking into consideration the influence of adsorbate-adsorbate, adsorbate-solvent interactions, and ion solvation explicitly. Long time scale MD simulations reveal coadsorbed H_ad/H₂O_ad on the surface. The free energy profiles for the Tafel and Heyrovsky type hydrogen coupling are extracted using umbrella sampling. It is found that the preferential mechanism can change depending on the surface coverage, highlighting the dual mechanistic nature for HER on Pt(111). Our work demonstrates the importance of controlling the solvent-substrate interactions in developing catalysts beyond Pt.

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.

Data-Driven Prediction of Formation Mechanisms of Lithium Ethylene Monocarbonate with an Automated Reaction Network

Interfacial reactions are notoriously difficult to characterize, and robust prediction of the chemical evolution and associated functionality of the resulting surface film is one of the grand challenges of materials chemistry. The solid-electrolyte interphase (SEI), critical to Li-ion batteries (LIBs), exemplifies such a surface film, and despite decades of work, considerable controversy remains regarding the major components of the SEI as well as their formation mechanisms. Here we use a reaction network to investigate whether lithium ethylene monocarbonate (LEMC) or lithium ethylene dicarbonate (LEDC) is the major organic component of the LIB SEI. Our data-driven, automated methodology is based on a systematic generation of relevant species using a general fragmentation/recombination procedure which provides the basis for a vast thermodynamic reaction landscape, calculated with density functional theory. The shortest pathfinding algorithms are employed to explore the reaction landscape and obtain previously proposed formation mechanisms of LEMC as well as several new reaction pathways and intermediates. For example, we identify two novel LEMC formation mechanisms: one which involves LiH generation and another that involves breaking the (CH₂)O-C(═O)OLi bond in LEDC. Most importantly, we find that all identified paths, which are also kinetically favorable under the explored conditions, require water as a reactant. This condition severely limits the amount of LEMC that can form, as compared with LEDC, a conclusion that has direct impact on the SEI formation in Li-ion energy storage systems. Finally, the data-driven framework presented here is generally applicable to any electrochemical system and expected to improve our understanding of surface passivation.

Dioxin and dibenzofuran like molecular analogues from the pyrolysis of biomass materials-the emerging challenge in bio-oil production

Introduction

The aggressive search for renewable energy resources and essential pyrosynthetic compounds has marked an exponential rise in the thermal degradation of biomass materials. Consequently, clean and sustainable transport fuels are increasingly desirable in a highly industrialized economy, for energy security and environmental protection. For this reason, biomass materials have been identified as promising alternatives to fossil fuels despite the challenges resulting from the possible formation of toxic nitrogen-based molecules during biomass degradation. In order to understand the free radical characteristic challenges facing the use of bio-oil, a brief review of the effects of free radicals in bio-oil is presented.

Methodology

Pyrolysis was conducted in a tubular flow quartz reactor at a residence time of 2 s at 1 atm. pressure, for a total pyrolysis time of 5 min. The thermal degradation of biomass components was investigated over the temperature range of 200 to 700 °C typically in 50 °C increments under two reaction conditions; pyrolysis in N₂ and oxidative pyrolysis in 5% O₂ in N₂. The pyrolysate effluent was analysed using a Gas chromatograph hyphenated to a mass selective detector (MSD).

Results

The yield of levoglucosan in the pyrolysis of cellulose in the entire pyrolysis temperature range was 68.2 wt % under inert conditions and 28.8 wt % under oxidative conditions. On the other hand, formaldehyde from pyrolysis of cellulose yielded 4 wt % while that from oxidative pyrolysis was 7 wt % translating to ⁓ 1.8 times higher than the yield from pyrolysis. Accordingly, we present for the first time dioxin-like and dibenzofuran-like nitrogenated analogues from an equimassic pyrolysis of cellulose and tyrosine. Levoglucosan and formaldehyde were completely inhibited during the equimassic pyrolysis of cellulose and tyrosine.

Conclusion

Clearly, any small amounts of N-biomass components such as amino acids in cellulosic biomass materials can inhibit the formation of levoglucosan–a major constituent of bio-oil. Overall, a judicious balance between the production of bio-oil and side products resulting from amino acids present in plant matter should be taken into account to minimize economic losses and mitigate against negative public health concerns.

Reaction prediction via atomistic simulation: from quantum mechanics to machine learning

It is an ultimate goal in chemistry to predict reaction without recourse to experiment. Reaction prediction is not just the reaction rate determination of known reactions but, more broadly, the reaction exploration to identify new reaction routes. This review briefly overviews the theory on chemical reaction and the current methods for computing/estimating reaction rate and exploring reaction space. We particularly focus on the atomistic simulation methods for reaction exploration, which are benefited significantly by recently emerged machine learning potentials. We elaborate the stochastic surface walking global pathway sampling based on the global neural network (SSW-NN) potential, developed in our group since 2013, which can explore complex reactions systems unbiasedly and automatedly. Two examples, molecular reaction and heterogeneous catalytic reactions, are presented to illustrate the current status for reaction prediction using SSW-NN.

Phase Transition Pathway Sampling via Swarm Intelligence and Graph Theory

The prediction of reaction pathways for solid-solid transformations remains a key challenge. Here, we develop a pathway sampling method via swarm intelligence and graph theory and demonstrate that our pallas method is an effective tool to help understand phase transformations in solid-state systems. The method is capable of finding low-energy transition pathways between two minima without having to specify any details of the transition mechanism a priori. We benchmarked our pallas method against known phase transitions in cadmium selenide (CdSe) and silicon (Si). pallas readily identifies previously reported, low-energy phase transition pathways for the wurtzite to rock-salt transition in CdSe and reveals a novel lower-energy pathway that has not yet been observed. In addition, pallas provides detailed information that explains the complex phase transition sequence observed during the decompression of Si from high pressure. Given the efficiency to identify low-barrier-energy reaction pathways, the pallas methodology represents a promising tool for materials by design with valuable insights for novel synthesis.

Understanding CO oxidation on the Pt(111) surface based on a reaction route network

Kinetic analysis by the rate constant matrix contraction on the reaction route network of CO oxidation on the Pt(111) surface obtained by the artificial force induced reaction reveals the impact of entropic contributions arising from a variety of local minima and transition states.

TGMin: An efficient global minimum searching program for free and surface-supported clusters

In this article, we introduce an efficient global-minimum structural search program named Tsinghua Global Minimum 2 (TGMin-2), which is the successor of the original TGMin algorithm that was developed in our group in 2011. We have introduced a number of new features and improvements into TGMin-2, including a symmetric structure generation algorithm that can produce good initial seeds for small- and medium-size clusters, the duplicated structure identification algorithm, and the improved structure adaption algorithm that was implemented in the original TGMin code. To predict the simulated photoelectron spectrum (PE spectrum) automatically, we also implemented a standalone program named AutoPES (Auto Photoelectron Spectroscopy), which can be used to simulate PE spectra and compare them with experimental results automatically. We have demonstrated that TGMin-2 and AutoPES are powerful tools for studying free and surface-supported molecules, clusters, and nanoclusters. © 2018 Wiley Periodicals, Inc.

A Trajectory-Based Method to Explore Reaction Mechanisms

The tsscds method, recently developed in our group, discovers chemical reaction mechanisms with minimal human intervention. It employs accelerated molecular dynamics, spectral graph theory, statistical rate theory and stochastic simulations to uncover chemical reaction paths and to solve the kinetics at the experimental conditions. In the present review, its application to solve mechanistic/kinetics problems in different research areas will be presented. Examples will be given of reactions involved in photodissociation dynamics, mass spectrometry, combustion chemistry and organometallic catalysis. Some planned improvements will also be described.

Uncertainty quantification for quantum chemical models of complex reaction networks

For the quantitative understanding of complex chemical reaction mechanisms, it is, in general, necessary to accurately determine the corresponding free energy surface and to solve the resulting continuous-time reaction rate equations for a continuous state space. For a general (complex) reaction network, it is computationally hard to fulfill these two requirements. However, it is possible to approximately address these challenges in a physically consistent way. On the one hand, it may be sufficient to consider approximate free energies if a reliable uncertainty measure can be provided. On the other hand, a highly resolved time evolution may not be necessary to still determine quantitative fluxes in a reaction network if one is interested in specific time scales. In this paper, we present discrete-time kinetic simulations in discrete state space taking free energy uncertainties into account. The method builds upon thermo-chemical data obtained from electronic structure calculations in a condensed-phase model. Our kinetic approach supports the analysis of general reaction networks spanning multiple time scales, which is here demonstrated for the example of the formose reaction. An important application of our approach is the detection of regions in a reaction network which require further investigation, given the uncertainties introduced by both approximate electronic structure methods and kinetic models. Such cases can then be studied in greater detail with more sophisticated first-principles calculations and kinetic simulations.

Implementation and performance of the artificial force induced reaction method in the GRRM17 program

This article reports implementation and performance of the artificial force induced reaction (AFIR) method in the upcoming 2017 version of GRRM program (GRRM17). The AFIR method, which is one of automated reaction path search methods, induces geometrical deformations in a system by pushing or pulling fragments defined in the system by an artificial force. In GRRM17, three different algorithms, that is, multicomponent algorithm (MC-AFIR), single-component algorithm (SC-AFIR), and double-sphere algorithm (DS-AFIR), are available, where the MC-AFIR was the only algorithm which has been available in the previous 2014 version. The MC-AFIR does automated sampling of reaction pathways between two or more reactant molecules. The SC-AFIR performs automated generation of global or semiglobal reaction path network. The DS-AFIR finds a single path between given two structures. Exploration of minimum energy structures within the hypersurface in which two different electronic states degenerate, and an interface with the quantum mechanics/molecular mechanics method, are also described. A code termed SAFIRE will also be available, as a visualization software for complicated reaction path networks. © 2017 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.

Pressure-induced silica quartz amorphization studied by iterative stochastic surface walking reaction sampling

The origin of the pressure-induced amorphization of SiO₂ is resolved from theory based on pathways on the global potential energy surface.

Crystal phase transition of urea: what governs the reaction kinetics in molecular crystal phase transitions

The predominant type-I hydrogen-bonding network in urea crystals facilitates the solid-to-solid transformation between major crystal forms.

Mechanism and microstructures in Ga2O3 pseudomartensitic solid phase transition

Solid-to-solid phase transition, although widely exploited in making new materials, challenges persistently our current theory for predicting its complex kinetics and rich microstructures in transition. The Ga2O3α-β phase transformation represents such a common but complex reaction with marked change in cation coordination and crystal density, which was known to yield either amorphous or crystalline products under different synthetic conditions. Here we, via recently developed stochastic surface walking (SSW) method, resolve for the first time the atomistic mechanism of Ga2O3α-β phase transformation, the pathway of which turns out to be the first reaction pathway ever determined for a new type of diffusionless solid phase transition, namely, pseudomartensitic phase transition. We demonstrate that the sensitivity of product crystallinity is caused by its multi-step, multi-type reaction pathway, which bypasses seven intermediate phases and involves all types of elementary solid phase transition steps, i.e. the shearing of O layers (martensitic type), the local diffusion of Ga atoms (reconstructive type) and the significant lattice dilation (dilation type). While the migration of Ga atoms across the close-packed O layers is the rate-determining step and yields "amorphous-like" high energy intermediates, the shearing of O layers contributes to the formation of coherent biphase junctions and the presence of a crystallographic orientation relation, (001)α//(201[combining macron])β + [120]α//[13[combining macron]2]β. Our experiment using high-resolution transmission electron microscopy further confirms the theoretical predictions on the atomic structure of biphase junction and the formation of (201[combining macron])β twin, and also discovers the late occurrence of lattice expansion in the nascent β phase that grows out from the parent α phase. By distinguishing pseudomartensitic transition from other types of mechanisms, we propose general rules to predict the product crystallinity of solid phase transition. The new knowledge on the kinetics of pseudomartensitic transition complements the theory of diffusionless solid phase transition.