1
|
Xiao Y, Zheng M, Li X, Ren C. Automated Skeleton Network Generation for ReaxFF Molecular Dynamics Simulations of Hydrocarbon Fuel Pyrolysis and Oxidation via a Rate-Based Algorithm. J Chem Theory Comput 2024; 20:5539-5557. [PMID: 38937883 DOI: 10.1021/acs.jctc.4c00409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024]
Abstract
In this study, we present an automated approach of rate-based skeleton network generation for ReaxFF MD simulation (RxMD-SN) for deriving the reaction kinetic mechanism of large hydrocarbon fuels in pyrolysis and oxidation from large-scale ReaxFF MD simulations. The approach contains the statistical calculation of reaction rate constants and the generation of skeleton reaction networks using a rate-based algorithm. The RxMD-SN method takes advantage of reaction flux ranking at a small time interval in terms of temporal reaction rate to extract the core reaction networks, which allows for keeping the rare reaction events that may be dominant in a certain period of the reaction network. The kinetic models derived from ReaxFF MD simulation in CH4 oxidation can reproduce what was obtained in the ReaxFF MD simulation, which demonstrates the capability of RxMD-SN in capturing the global reaction kinetics. An evaluation of reaction rate constants indicates that close kinetic parameters are shared for n-octane oxidation of similar reaction classes, shared oxidation reactions of CH4 against n-heptane, and shared pyrolysis reactions of the RP-3 surrogate fuel against n-heptane. This capability of RxMD-SN is particularly beneficial in meeting the challenges in characterizing the oxidation reaction kinetics of large hydrocarbon molecules. RxMD-SN approach is potentially a general approach in chemical kinetics modeling on the basis of ReaxFF MD simulations.
Collapse
Affiliation(s)
- Yuanyuan Xiao
- State Key Laboratory of Mesoscience and Engineering, Chinese Academy of Sciences, Beijing 100190, P. R. China
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
- Innovation Academy for Green Manufacture, Chinese Academy of Sciences, Beijing 100190, P. R. China
| | - Mo Zheng
- State Key Laboratory of Mesoscience and Engineering, Chinese Academy of Sciences, Beijing 100190, P. R. China
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
- Innovation Academy for Green Manufacture, Chinese Academy of Sciences, Beijing 100190, P. R. China
| | - Xiaoxia Li
- State Key Laboratory of Mesoscience and Engineering, Chinese Academy of Sciences, Beijing 100190, P. R. China
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
- Innovation Academy for Green Manufacture, Chinese Academy of Sciences, Beijing 100190, P. R. China
| | - Chunxing Ren
- State Key Laboratory of Mesoscience and Engineering, Chinese Academy of Sciences, Beijing 100190, P. R. China
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
- Innovation Academy for Green Manufacture, Chinese Academy of Sciences, Beijing 100190, P. R. China
| |
Collapse
|
2
|
Xu R, Meisner J, Chang AM, Thompson KC, Martínez TJ. First principles reaction discovery: from the Schrodinger equation to experimental prediction for methane pyrolysis. Chem Sci 2023; 14:7447-7464. [PMID: 37449065 PMCID: PMC10337770 DOI: 10.1039/d3sc01202f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 06/02/2023] [Indexed: 07/18/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Rui Xu
- Department of Chemistry, The PULSE Institute, Stanford University Stanford CA 94305 USA
- SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo Park CA 94025 USA
| | - Jan Meisner
- Department of Chemistry, The PULSE Institute, Stanford University Stanford CA 94305 USA
- SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo Park CA 94025 USA
| | - Alexander M Chang
- Department of Chemistry, The PULSE Institute, Stanford University Stanford CA 94305 USA
- SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo Park CA 94025 USA
| | - Keiran C Thompson
- Department of Chemistry, The PULSE Institute, Stanford University Stanford CA 94305 USA
- SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo Park CA 94025 USA
| | - Todd J Martínez
- Department of Chemistry, The PULSE Institute, Stanford University Stanford CA 94305 USA
- SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo Park CA 94025 USA
| |
Collapse
|
3
|
Dufour-Décieux V, Moakler C, Reed EJ, Cameron M. Predicting molecule size distribution in hydrocarbon pyrolysis using random graph theory. J Chem Phys 2023; 158:024101. [PMID: 36641405 DOI: 10.1063/5.0133641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
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.
Collapse
Affiliation(s)
- Vincent Dufour-Décieux
- Department of Materials Science and Engineering, Stanford University, Stanford, California 94305, USA
| | - Christopher Moakler
- Department of Mathematics, University of Maryland, College Park, Maryland 20742, USA
| | - Evan J Reed
- Department of Materials Science and Engineering, Stanford University, Stanford, California 94305, USA
| | - Maria Cameron
- Department of Mathematics, University of Maryland, College Park, Maryland 20742, USA
| |
Collapse
|
4
|
Dufour-Décieux V, Ransom B, Sendek AD, Freitas R, Blanchet J, Reed EJ. Temperature Extrapolation of Molecular Dynamics Simulations of Complex Chemistry to Microsecond Timescales Using Kinetic Models: Applications to Hydrocarbon Pyrolysis. J Chem Theory Comput 2022; 18:7496-7509. [PMID: 36399110 DOI: 10.1021/acs.jctc.2c00623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
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.
Collapse
Affiliation(s)
- Vincent Dufour-Décieux
- Department of Materials Science and Engineering, Stanford University, Stanford, California94305, United States
| | - Brandi Ransom
- Department of Materials Science and Engineering, Stanford University, Stanford, California94305, United States
| | - Austin D Sendek
- Department of Materials Science and Engineering, Stanford University, Stanford, California94305, United States.,Aionics, Inc., Palo Alto, California94301, United States
| | - Rodrigo Freitas
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
| | - Jose Blanchet
- Department of Management Science and Engineering, Stanford University, Stanford, California94305, United States
| | - Evan J Reed
- Department of Materials Science and Engineering, Stanford University, Stanford, California94305, United States
| |
Collapse
|
5
|
Kroonblawd MP, Goldman N, Maiti A, Lewicki JP. Polymer degradation through chemical change: a quantum-based test of inferred reactions in irradiated polydimethylsiloxane. Phys Chem Chem Phys 2022; 24:8142-8157. [PMID: 35332907 DOI: 10.1039/d1cp05647f] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Chemical reaction schemes are key conceptual tools for interpreting the results of experiments and simulations, but often carry implicit assumptions that remain largely unverified for complicated systems. Established schemes for chemical damage through crosslinking in irradiated silicone polymers comprised of polydimethylsiloxane (PDMS) date to the 1950's and correlate small-molecule off-gassing with specific crosslink features. In this regard, we use a somewhat reductionist model to develop a general conditional probability and correlation analysis approach that tests these types of causal connections between proposed experimental observables to reexamine this chemistry through quantum-based molecular dynamics (QMD) simulations. Analysis of the QMD simulations suggests that the established reaction schemes are qualitatively reasonable, but lack strong causal connections under a broad set of conditions that would enable making direct quantitative connections between off-gassing and crosslinking. Further assessment of the QMD data uncovers a strong (but nonideal) quantitative connection between exceptionally hard-to-measure chain scission events and the formation of silanol (Si-OH) groups. Our analysis indicates that conventional notions of radiation damage to PDMS should be further qualified and not necessarily used ad hoc. In addition, our efforts enable independent quantum-based tests that can inform confidence in assumed connections between experimental observables without the burden of fully elucidating entire reaction networks.
Collapse
Affiliation(s)
- Matthew P Kroonblawd
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA.
| | - Nir Goldman
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA.
| | - Amitesh Maiti
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA.
| | - James P Lewicki
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA.
| |
Collapse
|
6
|
Pineda M, Stamatakis M. Kinetic Monte Carlo simulations for heterogeneous catalysis: Fundamentals, current status, and challenges. J Chem Phys 2022; 156:120902. [DOI: 10.1063/5.0083251] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Kinetic Monte Carlo (KMC) simulations in combination with first-principles (1p)-based calculations are rapidly becoming the gold-standard computational framework for bridging the gap between the wide range of length scales and time scales over which heterogeneous catalysis unfolds. 1p-KMC simulations provide accurate insights into reactions over surfaces, a vital step toward the rational design of novel catalysts. In this Perspective, we briefly outline basic principles, computational challenges, successful applications, as well as future directions and opportunities of this promising and ever more popular kinetic modeling approach.
Collapse
Affiliation(s)
- M. Pineda
- Thomas Young Centre and Department of Chemical Engineering, University College London, Roberts Building, Torrington Place, London WC1E 7JE, United Kingdom
| | - M. Stamatakis
- Thomas Young Centre and Department of Chemical Engineering, University College London, Roberts Building, Torrington Place, London WC1E 7JE, United Kingdom
| |
Collapse
|
7
|
Clark AE, Adams H, Hernandez R, Krylov AI, Niklasson AMN, Sarupria S, Wang Y, Wild SM, Yang Q. The Middle Science: Traversing Scale In Complex Many-Body Systems. ACS CENTRAL SCIENCE 2021; 7:1271-1287. [PMID: 34471670 PMCID: PMC8393217 DOI: 10.1021/acscentsci.1c00685] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
A roadmap is developed that integrates simulation methodology and data science methods to target new theories that traverse the multiple length- and time-scale features of many-body phenomena.
Collapse
Affiliation(s)
- Aurora E. Clark
- Department of Chemistry, Washington State University, Pullman, Washington 99163, United States
| | - Henry Adams
- Department of Mathematics, Colorado State
University, Fort Collins, Colorado 80523, United States
| | - Rigoberto Hernandez
- Departments
of Chemistry, Chemical and Biomolecular Engineering, and Materials
Science and Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Anna I. Krylov
- Department of Chemistry, University of Southern California, Los Angeles, California 90089, United States
| | - Anders M. N. Niklasson
- Theoretical
Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Sapna Sarupria
- Department of Chemical and Biomolecular Engineering, Center for Optical
Materials Science and Engineering Technologies (COMSET), Clemson University, Clemson, South Carolina 29670, United States
- Department
of Chemistry, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Yusu Wang
- Halıcıŏglu Data Science Institute, University of California, San Diego, La Jolla, California 92093, United States
| | - Stefan M. Wild
- Mathematics
and Computer Science Division, Argonne National
Laboratory, Lemont, Illinois 60439, United
States
| | - Qian Yang
- Computer Science and Engineering Department, University of Connecticut, Storrs, Connecticut 06269-4155, United States
| |
Collapse
|
8
|
Dufour-Décieux V, Freitas R, Reed EJ. Atomic-Level Features for Kinetic Monte Carlo Models of Complex Chemistry from Molecular Dynamics Simulations. J Phys Chem A 2021; 125:4233-4244. [PMID: 33973780 DOI: 10.1021/acs.jpca.1c00942] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
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.
Collapse
Affiliation(s)
- Vincent Dufour-Décieux
- Department of Materials Science and Engineering, Stanford University, Stanford, California 94305, United States
| | - Rodrigo Freitas
- Department of Materials Science and Engineering, Stanford University, Stanford, California 94305, United States.,Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Evan J Reed
- Department of Materials Science and Engineering, Stanford University, Stanford, California 94305, United States
| |
Collapse
|
9
|
Robertson C, Habershon S. Simple position and orientation preconditioning scheme for minimum energy path calculations. J Comput Chem 2021; 42:761-770. [PMID: 33617652 DOI: 10.1002/jcc.26495] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 01/16/2021] [Accepted: 01/22/2021] [Indexed: 11/08/2022]
Abstract
Minimum-energy path (MEP) calculations, such as those typified by the nudged elastic band method, require input of reactant and product molecular configurations at initialization. In the case of reactions involving more than one molecule, generating initial reactant and product configurations requires careful consideration of the relative position and orientations of the reactive molecules in order to ensure that the resulting MEP calculation proceeds without converging on an alternative reaction-path, and without requiring excessive numbers of optimization iterations; as such, this initial system set-up is most commonly performed "by hand," with an expert user arranging reactive molecules in space to ensure that the following MEP calculation runs smoothly. In this Article, we introduce a simple preconditioning scheme which replaces this labor-intensive, human-knowledge-based step with an automated deterministic computational scheme. In our approach, initial reactant and product configurations are generated such that steric hindrance between reactive molecules is minimized in the reactant and product configurations, while also simultaneously requiring minimal structural differences between the reactants and products. The method is demonstrated using a benchmark test-set of >3400 organic molecular reactions, where comparison of the reactant/product configurations generated using our approach compare very well to initial configurations which were generated on an ad hoc basis.
Collapse
Affiliation(s)
- Christopher Robertson
- Department of Chemistry and Centre for Scientific Computing, University of Warwick, Coventry, UK
| | - Scott Habershon
- Department of Chemistry and Centre for Scientific Computing, University of Warwick, Coventry, UK
| |
Collapse
|
10
|
Xu L, Zhu FX. A new way to develop reaction network automatically via DFT-based adaptive kinetic Monte Carlo. Chem Eng Sci 2020. [DOI: 10.1016/j.ces.2020.115746] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
11
|
Rice BM, Mattson WD, Larentzos JP, Byrd EFC. Heuristics for chemical species identification in dense systems. J Chem Phys 2020; 153:064102. [DOI: 10.1063/5.0015664] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Affiliation(s)
- Betsy M. Rice
- US Army CCDC Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, Maryland 21005, USA
| | - William D. Mattson
- US Army CCDC Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, Maryland 21005, USA
| | - James P. Larentzos
- US Army CCDC Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, Maryland 21005, USA
| | - Edward F. C. Byrd
- US Army CCDC Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, Maryland 21005, USA
| |
Collapse
|