Learning reduced kinetic Monte Carlo models of complex chemistry from molecular dynamics

Construction and Experimental Validation of Embedded Potential Functions for Ta-Re Alloys

Ta/Re layered composite material is a high-temperature material composed of the refractory metal tantalum (Ta) as the matrix and high-melting-point, high-strength rhenium (Re) as the reinforcement layer. It holds significant potential for application in aerospace engine nozzles. Developing the Ta/Re potential function is crucial for understanding the diffusion behavior at the Ta/Re interface and elucidating the high-temperature strengthening and toughening mechanism of Ta/Re layered composites. In this paper, the embedded atom method (EAM) potential function for tantalum/rhenium binary alloys (Ta-Re alloys) is derived using the force-matching method and validated through first-principles calculations and experimental characterization. The results show that for the lattice constant of a bcc structure containing 54 atoms, surface formation energies per unit area of Ta-Re alloys obtained based on the potential function are 12.196 Å, E100 = 0.16 × 10-2 eV, E110 = 0.10 × 10-2 eV, and E111 = 0.08 × 10-2 eV, with error values of 0.015 Å, 0.04 × 10-2 eV, 0.02 × 10-2 eV, and 0.01 × 10-2 eV, respectively, compared with the calculations from first principles calculations. It is noteworthy that the errors in the average binding energies of Ta-rich (Ta39Re20, where the number of Ta atoms is 39 and Re atoms is 20) and Re-rich (Ta20Re39, where the number of Ta atoms is 20 and Re atoms is 39) cluster atoms, calculated by the potential function and first-principles methods, are only 1.64% to 1.98%. These results demonstrate the accuracy of the constructed EAM potential function. Based on this, three compositions of Ta-Re alloys (Ta48Re6, Ta30Re24, and Ta6Re48; the numerical subscripts represent the number of atoms of each corresponding element) were randomly synthesized, and a comparative analysis of their bulk moduli was conducted. The results revealed that the experimental values of the bulk modulus showed a decreasing and then an increasing tendency with the calculated values, which indicated that the potential function has a very good generalization ability. This study can provide theoretical guidance for the modulation of Ta/Re laminate composite properties.

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

Our recent success in exploiting graphical processing units (GPUs) to accelerate quantum chemistry computations led to the development of the ab initio nanoreactor, a computational framework for automatic reaction discovery and kinetic model construction. In this work, we apply the ab initio nanoreactor to methane pyrolysis, from automatic reaction discovery to path refinement and kinetic modeling. Elementary reactions occurring during methane pyrolysis are revealed using GPU-accelerated ab initio molecular dynamics simulations. Subsequently, these reaction paths are refined at a higher level of theory with optimized reactant, product, and transition state geometries. Reaction rate coefficients are calculated by transition state theory based on the optimized reaction paths. The discovered reactions lead to a kinetic model with 53 species and 134 reactions, which is validated against experimental data and simulations using literature kinetic models. We highlight the advantage of leveraging local brute force and Monte Carlo sensitivity analysis approaches for efficient identification of important reactions. Both sensitivity approaches can further improve the accuracy of the methane pyrolysis kinetic model. The results in this work demonstrate the power of the ab initio nanoreactor framework for computationally affordable systematic reaction discovery and accurate kinetic modeling.

Predicting molecule size distribution in hydrocarbon pyrolysis using random graph theory

Hydrocarbon pyrolysis is a complex process involving large numbers of chemical species and types of chemical reactions. Its quantitative description is important for planetary sciences, in particular, for understanding the processes occurring in the interior of icy planets, such as Uranus and Neptune, where small hydrocarbons are subjected to high temperature and pressure. We propose a computationally cheap methodology based on an originally developed ten-reaction model and the configurational model from random graph theory. This methodology generates accurate predictions for molecule size distributions for a variety of initial chemical compositions and temperatures ranging from 3200 to 5000 K. Specifically, we show that the size distribution of small molecules is particularly well predicted, and the size of the largest molecule can be accurately predicted provided that this molecule is not too large.

Chemical reaction networks and opportunities for machine learning

Chemical reaction networks (CRNs), defined by sets of species and possible reactions between them, are widely used to interrogate chemical systems. To capture increasingly complex phenomena, CRNs can be leveraged alongside data-driven methods and machine learning (ML). In this Perspective, we assess the diverse strategies available for CRN construction and analysis in pursuit of a wide range of scientific goals, discuss ML techniques currently being applied to CRNs and outline future CRN-ML approaches, presenting scientific and technical challenges to overcome.

Temperature Extrapolation of Molecular Dynamics Simulations of Complex Chemistry to Microsecond Timescales Using Kinetic Models: Applications to Hydrocarbon Pyrolysis

We develop a method to construct temperature-dependent kinetic models of hydrocarbon pyrolysis, based on information from molecular dynamics (MD) simulations of pyrolyzing systems in the high-temperature regime. MD simulations are currently a key tool to understand the mechanism of complex chemical processes such as pyrolysis and to observe their outcomes in different conditions, but these simulations are computationally expensive and typically limited to nanoseconds of simulation time. This limitation is inconsequential at high temperatures, where equilibrium is reached quickly, but at low temperatures, the system may not equilibrate within a tractable simulation timescale. In this work, we develop a method to construct kinetic models of hydrocarbon pyrolysis using the information from the high-temperature high-reactivity regime. We then extrapolate this model to low temperatures, which enables microsecond-long simulations to be performed. We show that this approach accurately predicts the time evolution of small molecules, as well as the size and composition of long carbon chains across a wide range of temperatures and compositions. Further, we show that the range of suitable temperatures for extrapolation can easily be improved by adding more simulations to the training data. Compared to experimental results, our kinetic model leads to similar compositional trends while allowing for more detailed kinetic and mechanistic insights.

Atomic-Level Features for Kinetic Monte Carlo Models of Complex Chemistry from Molecular Dynamics Simulations

The high computational cost of evaluating atomic interactions recently motivated the development of computationally inexpensive kinetic models, which can be parameterized from molecular dynamics (MD) simulations of the complex chemistry of thousands of species or other processes and accelerate the prediction of the chemical evolution by up to four orders of magnitude. Such models go beyond the commonly employed potential energy surface fitting methods in that they are aimed purely at describing kinetic effects. So far, such kinetic models utilize molecular descriptions of reactions and have been constrained to only reproduce molecules previously observed in MD simulations. Therefore, these descriptions fail to predict the reactivity of unobserved molecules, for example, in the case of large molecules or solids. Here, we propose a new approach for the extraction of reaction mechanisms and reaction rates from MD simulations, namely, the use of atomic-level features. Using the complex chemical network of hydrocarbon pyrolysis as an example, it is demonstrated that kinetic models built using atomic features are able to explore chemical reaction pathways never observed in the MD simulations used to parameterize them, a critical feature to describe rare events. Atomic-level features are shown to construct reaction mechanisms and estimate reaction rates of unknown molecular species from elementary atomic events. Through comparisons of the model ability to extrapolate to longer simulation time scales and different chemical compositions than the ones used for parameterization, it is demonstrated that kinetic models employing atomic features retain the same level of accuracy and transferability as the use of features based on molecular species, while being more compact and parameterized with less data. We also find that atomic features can better describe the formation of large molecules enabling the simultaneous description of small molecules and condensed phases.

Rapid data-driven model reduction of nonlinear dynamical systems including chemical reaction networks using ℓ1-regularization

Large-scale nonlinear dynamical systems, such as models of atmospheric hydrodynamics, chemical reaction networks, and electronic circuits, often involve thousands or more interacting components. In order to identify key components in the complex dynamical system as well as to accelerate simulations, model reduction is often desirable. In this work, we develop a new data-driven method utilizing ℓ¹-regularization for model reduction of nonlinear dynamical systems, which involves minimal parameterization and has polynomial-time complexity, allowing it to easily handle large-scale systems with as many as thousands of components in a matter of minutes. A primary objective of our model reduction method is interpretability, that is to identify key components of the dynamical system that contribute to behaviors of interest, rather than just finding an efficient projection of the dynamical system onto lower dimensions. Our method produces a family of reduced models that exhibit a trade-off between model complexity and estimation error. We find empirically that our method chooses reduced models with good extrapolation properties, an important consideration in practical applications. The reduction and extrapolation performance of our method are illustrated by applications to the Lorenz model and chemical reaction rate equations, where performance is found to be competitive with or better than state-of-the-art approaches.