Importance sampling within configuration space integration for adsorbate thermophysical properties: a case study for CH3/Ni(111).

A new strategy is presented for computing anharmonic partition functions for the motion of adsorbates relative to a catalytic surface. Importance sampling is compared with conventional Monte Carlo. The importance sampling is significantly more efficient. This new approach is applied to CH3* on Ni(111) as a test case. The motion of methyl relative to the nickel surface is found to be anharmonic, with significantly higher entropy compared to the standard harmonic oscillator model. The new method is freely available as part of the Minima-Preserving Neural Network within the ADTHERM package.

Accurate Enthalpies of Formation for PFAS from First-Principles: Combining Different Levels of Theory in a Generalized Thermochemical Hierarchy.

The enthalpies of formation are computed for a large number of per- and poly fluoroalkyl substances (PFAS) using a connectivity-based hierarchy (CBH) approach. A combination of different electronic structure methods are used to provide the reference data in a hierarchical manner. The ANL0 method, in conjunction with the active thermochemical tables, provides enthalpies of formation for smaller species with subchemical accuracy. Coupled-cluster theory with explicit correlations are used to compute enthalpies of formation for intermediate species, based upon the ANL0 results. For the largest PFAS, including perfluorooctanoic acid (PFOA) and heptafluoropropylene oxide dimer acid (GenX), coupled-cluster theory with local correlations is used. The sequence of homodesmotic reactions proposed by the CBH are determined automatically by a new open-source code, AutoCBH. The results are the first reported enthalpies of formation for the majority of the species. A convergence analysis and global uncertainty quantification confirm that the enthalpies of formation at 0 K should be accurate to within ±5 kJ/mol. This new approach is not limited to PFAS, but can be applied to many chemical systems.

Metal-Free Reversible Double Cyclization of Cyanuric Diazide to an Asymmetric Bitetrazolate via Cleavage of the Six-Membered Aromatic Ring.

Crystallization of the reaction mixture of 2-amino-4,6-diazido-1,3,5-triazine and excess tert-butylamine results in the isolation of tert-butylammonium N,N-[1'H-(1,5'-bitetrazol)-5-yl]cyanamidate, suggesting a complex decyclization/cyclization rearrangement involving breakage of the six-membered aromatic ring and the formation of two new five-membered azole rings mediated by deprotonation of the precursor by the amine. The addition of tert-butylamine to 2-amino-4,6-diazido-1,3,5-triazine gives spectroscopic indication of thermodynamically unfavorable reactivity in low-dielectric solvents, and high-level quantum chemical computations also suggest its formation to be unfavorable. A computed interconversion pathway describes the likely reaction mechanism and supports the general thermodynamic unfavorability of the reaction and the requirement for a high-dielectric environment to template formation of the ionic product and its trapping by crystallization.

High-Dimensional Neural Network Potentials for Accurate Prediction of Equation of State: A Case Study of Methane.

Machine learning-based interatomic potentials, such as those provided by neural networks, are increasingly important in molecular dynamics simulations. In the present work, we consider the applicability and robustness of machine learning molecular dynamics to predict the equation of state properties of methane by using high-dimensional neural network potentials (HDNNPs). We investigate two different strategies for generating training data: one strategy based upon bulk representations using periodic cells and another strategy based upon clusters of molecules. We assess the accuracy of the trained potentials by predicting the equilibrium mass density for a wide range of thermodynamic conditions to characterize the liquid phase, supercritical fluid, and gas phase, as well as the liquid-vapor coexistence curve. Our results show an excellent agreement with reference phase diagrams, with an average error below ∼2% for all studied phases. Moreover, we confirm the applicability of models trained on cluster data sets for producing accurate and reliable results.

Estimating Free Energy Barriers for Heterogeneous Catalytic Reactions with Machine Learning Potentials and Umbrella Integration.

Predicting the rate constants of elementary reaction steps is key for the computational modeling of catalytic processes. Within transition state theory (TST), this requires an accurate estimation of the corresponding free energy barriers. While sophisticated methods for estimating free energy differences exist, these typically require extensive (biased) molecular dynamics simulations that are computationally prohibitive with the first-principles electronic structure methods that are typically used in catalysis research. In this contribution, we show that machine-learning (ML) interatomic potentials can be trained in an automated iterative workflow to perform such free energy calculations at a much reduced computational cost as compared to a direct density functional theory (DFT) based evaluation. For the decomposition of CHO on Rh(111), we find that thermal effects are substantial and lead to a decrease in the free energy barrier, which can be vanishingly small, depending on the DFT functional used. This is in stark contrast to previously reported estimates based on a harmonic TST approximation, which predicted an increase in the barrier at elevated temperatures. Since CHO is the reactant of the putative rate limiting reaction step in syngas conversion on Rh(111) and essential for the selectivity toward oxygenates containing multiple carbon atoms (C2+ oxygenates), our results call into question the reported mechanism established by microkinetic models.

Automated Generation of Microkinetics for Heterogeneously Catalyzed Reactions Considering Correlated Uncertainties.

The study presents an ab-initio based framework for the automated construction of microkinetic mechanisms considering correlated uncertainties in all energetic parameters and estimation routines. 2000 unique microkinetic models were generated within the uncertainty space of the BEEF-vdW functional for the oxidation reactions of representative exhaust gas emissions from stoichiometric combustion engines over Pt(111) and compared to experiments through multiscale modeling. The ensemble of simulations stresses the importance of considering uncertainties. Within this set of first-principles-based models, it is possible to identify a microkinetic mechanism that agrees with experimental data. This mechanism can be traced back to a single exchange-correlation functional, and it suggests that Pt(111) could be the active site for the oxidation of light hydrocarbons. The study provides a universal framework for the automated construction of reaction mechanisms with correlated uncertainty quantification, enabling a DFT-constrained microkinetic model optimization for other heterogeneously catalyzed systems.

Linking Experimental and Ab Initio Thermochemistry of Adsorbates with a Generalized Thermochemical Hierarchy.

Enthalpies of formation of adsorbates are crucial parameters in the microkinetic modeling of heterogeneously catalyzed reactions since they quantify the stability of intermediates on the catalyst surface. This quantity is often computed using density functional theory (DFT), as more accurate methods are computationally still too expensive, which means that the derived enthalpies have a large uncertainty. In this study, we propose a new error cancellation method to compute the enthalpies of formation of adsorbates from DFT more accurately through a generalized connectivity-based hierarchy. The enthalpy of formation is determined through a hypothetical reaction that preserves atomistic and bonding environments. The method is applied to a data set of 60 adsorbates on Pt(111) with up to 4 heavy (non-hydrogen) atoms. Enthalpies of formation of the fragments required for the bond balancing reactions are based on experimental heats of adsorption for Pt(111). The comparison of enthalpies of formation derived from different DFT functionals using the isodesmic reactions shows that the effect of the functional is significantly reduced due to the error cancellation. Thus, the proposed methodology creates an interconnected thermochemical network of adsorbates that combines experimental with ab initio thermochemistry in a single and more accurate thermophysical database.

Theory-Based Mechanism for Fluoromethane Combustion I: Thermochemistry and Abstraction Reactions.

A new detailed chemical kinetic mechanism is presented for small fluorinated hydrocarbons. Ab initio electronic structure theory is used to provide heats of formation with subchemical accuracy. The ANL0 method is extended to include fluorine. The resulting heats of formation at 0 K are in excellent agreement with 36 benchmark species in the Active Thermochemical Tables, with a mean error of µ = -0.02 kJ/mol and a standard deviation of σ = 0.91 kJ/mol. The thermophysical properties for 92 small-molecule H/C/O/F species are computed. The rate coefficients for 40+ H-abstraction reactions involving H, O, F, OH, OF, HO2, and various methyl radicals with CH4, CH3F, CH2F2, CHF3, CH2O, and CHFO are discussed. The computed rate constants are in excellent agreement with the available literature. Additionally, 30+ rate constants are provided for F abstraction, which are several orders of magnitude smaller than H abstraction. The thermophysical properties and rate constants are provided in a mechanism. This mechanism is the first in a series of theory-based investigations into the thermal destruction of per- and polyfluorinated species.

RMG Database for Chemical Property Prediction.

The Reaction Mechanism Generator (RMG) database for chemical property prediction is presented. The RMG database consists of curated datasets and estimators for accurately predicting the parameters necessary for constructing a wide variety of chemical kinetic mechanisms. These datasets and estimators are mostly published and enable prediction of thermodynamics, kinetics, solvation effects, and transport properties. For thermochemistry prediction, the RMG database contains 45 libraries of thermochemical parameters with a combination of 4564 entries and a group additivity scheme with 9 types of corrections including radical, polycyclic, and surface absorption corrections with 1580 total curated groups and parameters for a graph convolutional neural network trained using transfer learning from a set of >130 000 DFT calculations to 10 000 high-quality values. Correction schemes for solvent-solute effects, important for thermochemistry in the liquid phase, are available. They include tabulated values for 195 pure solvents and 152 common solutes and a group additivity scheme for predicting the properties of arbitrary solutes. For kinetics estimation, the database contains 92 libraries of kinetic parameters containing a combined 21 000 reactions and contains rate rule schemes for 87 reaction classes trained on 8655 curated training reactions. Additional libraries and estimators are available for transport properties. All of this information is easily accessible through the graphical user interface at https://rmg.mit.edu. Bulk or on-the-fly use can be facilitated by interfacing directly with the RMG Python package which can be installed from Anaconda. The RMG database provides kineticists with easy access to estimates of the many parameters they need to model and analyze kinetic systems. This helps to speed up and facilitate kinetic analysis by enabling easy hypothesis testing on pathways, by providing parameters for model construction, and by providing checks on kinetic parameters from other sources.

Quantifying the Impact of Parametric Uncertainty on Automatic Mechanism Generation for CO2 Hydrogenation on Ni(111).

Automatic mechanism generation is used to determine mechanisms for the CO2 hydrogenation on Ni(111) in a two-stage process while considering the correlated uncertainty in DFT-based energetic parameters systematically. In a coarse stage, all the possible chemistry is explored with gas-phase products down to the ppb level, while a refined stage discovers the core methanation submechanism. Five thousand unique mechanisms were generated, which contain minor perturbations in all parameters. Global uncertainty assessment, global sensitivity analysis, and degree of rate control analysis are performed to study the effect of this parametric uncertainty on the microkinetic model predictions. Comparison of the model predictions with experimental data on a Ni/SiO2 catalyst find a feasible set of microkinetic mechanisms within the correlated uncertainty space that are in quantitative agreement with the measured data, without relying on explicit parameter optimization. Global uncertainty and sensitivity analyses provide tools to determine the pathways and key factors that control the methanation activity within the parameter space. Together, these methods reveal that the degree of rate control approach can be misleading if parametric uncertainty is not considered. The procedure of considering uncertainties in the automated mechanism generation is not unique to CO2 methanation and can be easily extended to other challenging heterogeneously catalyzed reactions.

Investigating the role of low level reinforcement reflex loops in insect locomotion.

Insects are highly capable walkers, but many questions remain regarding how the insect nervous system controls locomotion. One particular question is how information is communicated between the 'lower level' ventral nerve cord (VNC) and the 'higher level' head ganglia to facilitate control. In this work, we seek to explore this question by investigating how systems traditionally described as 'positive feedback' may initiate and maintain stepping in the VNC with limited information exchanged between lower and higher level centers. We focus on the 'reflex reversal' of the stick insect femur-tibia joint between a resistance reflex (RR) and an active reaction in response to joint flexion, as well as the activation of populations of descending dorsal median unpaired (desDUM) neurons from limb strain as our primary reflex loops. We present the development of a neuromechanical model of the stick insect (Carausius morosus) femur-tibia (FTi) and coxa-trochanter joint control networks 'in-the-loop' with a physical robotic limb. The control network generates motor commands for the robotic limb, whose motion and forces generate sensory feedback for the network. We based our network architecture on the anatomy of the non-spiking interneuron joint control network that controls the FTi joint, extrapolated network connectivity based on known muscle responses, and previously developed mechanisms to produce 'sideways stepping'. Previous studies hypothesized that RR is enacted by selective inhibition of sensory afferents from the femoral chordotonal organ, but no study has tested this hypothesis with a model of an intact limb. We found that inhibiting the network's flexion position and velocity afferents generated a reflex reversal in the robot limb's FTi joint. We also explored the intact network's ability to sustain steady locomotion on our test limb. Our results suggested that the reflex reversal and limb strain reinforcement mechanisms are both necessary but individually insufficient to produce and maintain rhythmic stepping in the limb, which can be initiated or halted by brief, transient descending signals. Removing portions of this feedback loop or creating a large enough disruption can halt stepping independent of the higher-level centers. We conclude by discussing why the nervous system might control motor output in this manner, as well as how to apply these findings to generalized nervous system understanding and improved robotic control.

Diastereomers and Low-Temperature Oxidation.

Diastereomers have historically been ignored when building kinetic mechanisms for combustion. Low-temperature oxidation kinetics, which continues to gain interest in both combustion and atmospheric communities, may be affected by the inclusion of diastereomers in radical chain-branching pathways. In this work, key intermediates and transition states lacking stereochemical specification in an existing diethyl ether low-temperature oxidation mechanism were replaced with their diastereomeric counterparts. Rate coefficients for reactions involving diastereomers were computed with ab initio transition state theory master equation calculations. The presence of diastereomers increased rate coefficients by factors of 1.2-1.6 across various temperatures and pressures. Ignition delay simulations incorporating these revised rate coefficients indicate that the diastereomers enhanced the overall reactivity of the mechanism by almost 15% and increased the peak ketohydroperoxide concentration by 30% in the negative temperature coefficient region at combustion-relevant pressures. These results provide an illustrative indication of the important role of stereomeric effects in oxidation kinetics.

Heterogeneity in susceptibility dictates the order of epidemic models.

The fundamental models of epidemiology describe the progression of an infectious disease through a population using compartmentalized differential equations, but typically do not incorporate population-level heterogeneity in infection susceptibility. Here we combine a generalized analytical framework of contagion with computational models of epidemic dynamics to show that variation strongly influences the rate of infection, while the infection process simultaneously sculpts the susceptibility distribution. These joint dynamics influence the force of infection and are, in turn, influenced by the shape of the initial variability. We find that certain susceptibility distributions (the exponential and the gamma) are unchanged through the course of the outbreak, and lead naturally to power-law behavior in the force of infection; other distributions are often sculpted towards these "eigen-distributions" through the process of contagion. The power-law behavior fundamentally alters predictions of the long-term infection rate, and suggests that first-order epidemic models that are parameterized in the exponential-like phase may systematically and significantly over-estimate the final severity of the outbreak. In summary, our study suggests the need to examine the shape of susceptibility in natural populations as part of efforts to improve prediction models and to prioritize interventions that leverage heterogeneity to mitigate against spread.

Reaction Mechanism Generator v3.0: Advances in Automatic Mechanism Generation.

In chemical kinetics research, kinetic models containing hundreds of species and tens of thousands of elementary reactions are commonly used to understand and predict the behavior of reactive chemical systems. Reaction Mechanism Generator (RMG) is a software suite developed to automatically generate such models by incorporating and extrapolating from a database of known thermochemical and kinetic parameters. Here, we present the recent version 3 release of RMG and highlight improvements since the previously published description of RMG v1.0. Most notably, RMG can now generate heterogeneous catalysis models in addition to the previously available gas- and liquid-phase capabilities. For model analysis, new methods for local and global uncertainty analysis have been implemented to supplement first-order sensitivity analysis. The RMG database of thermochemical and kinetic parameters has been significantly expanded to cover more types of chemistry. The present release includes parallelization for faster model generation and a new molecule isomorphism approach to improve computational performance. RMG has also been updated to use Python 3, ensuring compatibility with the latest cheminformatics and machine learning packages. Overall, RMG v3.0 includes many changes which improve the accuracy of the generated chemical mechanisms and allow for exploration of a wider range of chemical systems.

A continuous flow liquid propellant strand burner for high pressure monopropellant and bipropellant combustion studies.

A combustion assembly capable of continuously burning monopropellant and bipropellant liquid fuels at pressures up to 80 bars (1145 psig) was designed and constructed. The assembly is based on a liquid propellant strand burner where a manifold maintains small positive differential pressures on the fuel to maintain a steady supply into the reaction vessel. Optical ports enable direct visualization of the flame and will allow for future spectroscopic and imaging studies of the flame. The strand burner design was tested using nitromethane with both air and inert environments in the reaction vessel. Continuous combustion was sustained for almost 8 min in air (34 bars/500 psig) and more than 6 min in N2 (70 bars/1000 psig). A unique outcome from the initial testing of this device is the ability to ignite liquid nitromethane in an inert environment without the use of a pilot flame started in air.

Neurodynamic modeling of the fruit fly Drosophila melanogaster.

This manuscript describes neuromechanical modeling of the fruit fly Drosophila melanogaster in the form of a hexapod robot, Drosophibot, and an accompanying dynamic simulation. Drosophibot is a testbed for real-time dynamical neural controllers modeled after the anatomy and function of the insect nervous system. As such, Drosophibot has been designed to capture features of the animal's biomechanics in order to better test the neural controllers. These features include: dynamically scaling the robot to match the fruit fly by designing its joint elasticity and movement speed; a biomimetic actuator control scheme that converts neural activity into motion in the same way as observed in insects; biomimetic sensing, including proprioception from all leg joints and strain sensing from all leg segments; and passively compliant tarsi that mimic the animal's passive compliance to the walking substrate. We incorporated these features into a dynamical simulation of Drosophibot, and demonstrate that its actuators and sensors perform in an animal-like way. We used this simulation to test a neural walking controller based on anatomical and behavioral data from insects. Finally, we describe Drosophibot's hardware and show that the animal-like features of the simulation transfer to the physical robot.

Thermal Stability of Bis-Tetrazole and Bis-Triazole Derivatives with Long Catenated Nitrogen Chains: Quantitative Insights from High-Level Quantum Chemical Calculations.

Azobis tetrazole and triazole derivatives containing long catenated nitrogen atom chains are of great interest as promising green energetic materials. However, these compounds often exhibit poor thermal stability and high impact sensitivity. Kinetics and mechanism of the primary decomposition reactions are directly related to these issues. In the present work, with the aid of highly accurate CCSD(T)-F12 quantum chemical calculations, we obtained reliable bond dissociation energies and activation barriers of thermolysis reactions for a number of N-rich heterocycles. We studied all existing 1,1'-azobistetrazoles containing an N10 chain, their counterparts with the 5,5'-bridging pattern, and the species with hydrazo- and azoxy-bridges, which are often present energetic moieties. The N8-containing azobistriazole was considered as well. For all compounds studied, the radical decomposition channel was found to be kinetically unfavorable. All species decompose via the ring-opening reaction yielding a transient azide (or diazo) intermediate followed by the N2 elimination. In the case of azobistetrazole derivatives, the calculated effective activation barriers of decomposition are ∼26-33 kcal mol-1, which is notably lower than that of tetrazole (∼40 kcal mol-1). This fact agrees well with the low thermal stability and high impact sensitivities of the former species. The activation barriers of the N2 elimination were found to be almost the same for the azobis compounds and the parent tetrazole, and the effective decomposition barrier is determined by the thermodynamics of the tetrazole-azide rearrangement. In comparison with 1,1'-azobistetrazole, the hydrazo-bridged compound is more stable kinetically due to the lack of pi-conjugation in the azide intermediate. In turn, the azoxy-bridged compounds are entirely unstable due to tremendous azide stabilization by the hydrogen bond formation. In general, the 5,5'-bridged species are more thermally stable than their 1,1'-counterparts due to a much higher barrier of the N2 elimination. Apart from this, the highly accurate gas-phase formation enthalpies were calculated at the W1-F12 level of theory for all species studied.

Accelerating Variational Transition State Theory via Artificial Neural Networks.

An application of atomistic machine learning for variational transition state theory is presented. The rate constants for various radical-radical reactions are computed using variable reaction coordinate transition state theory. In order to simplify the calculation process, artificial neural networks are trained on a structured grid of potential energy data. The resulting surrogate potential energy surface is used in classical phase space representations to describe the interaction between two radical species in the gas phase. When the artificial neural network is trained to potential energy alone, the number of explicit electronic structure energy calculations required to compute a rate constant decreases by at least a factor of 4. When forces are included in the training data, the reduction is more dramatic-at least an order of magnitude.