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Wu H, Payne AM, Pang HW, Menon A, Grambow CA, Ranasinghe DS, Dong X, Grinberg Dana A, Green WH. Toward Accurate Quantum Mechanical Thermochemistry: (1) Extensible Implementation and Comparison of Bond Additivity Corrections and Isodesmic Reactions. J Phys Chem A 2024; 128:4335-4352. [PMID: 38752854 DOI: 10.1021/acs.jpca.4c00949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/31/2024]
Abstract
Obtaining accurate enthalpies of formation of chemical species, ΔHf, often requires empirical corrections that connect the results of quantum mechanical (QM) calculations with the experimental enthalpies of elements in their standard state. One approach is to use atomization energy corrections followed by bond additivity corrections (BACs), such as those defined by Petersson et al. or Anantharaman and Melius. Another approach is to utilize isodesmic reactions (IDRs) as shown by Buerger et al. We implement both approaches in Arkane, an open-source software that can calculate species thermochemistry using results from various QM software packages. In this work, we collect 421 reference species from the literature to derive ΔHf corrections and fit atomization energy corrections and BACs for 15 commonly used model chemistries. We find that both types of BACs yield similar accuracy, although Anantharaman- and Melius-type BACs appear to generalize better. Furthermore, BACs tend to achieve better accuracy than IDRs for commonly used model chemistries, and IDRs can be less robust because of the sensitivity to the chosen reference species and reactions. Overall, Anantharaman- and Melius-type BACs are our recommended approach for achieving accurate QM corrections for enthalpies.
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Affiliation(s)
- Haoyang Wu
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - A Mark Payne
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Hao-Wei Pang
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Angiras Menon
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Colin A Grambow
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Duminda S Ranasinghe
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Xiaorui Dong
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Alon Grinberg Dana
- Wolfson Department of Chemical Engineering and Grand Technion Energy Program, Technion─Israel Institute of Technology, Haifa 3200003, Israel
| | - William H Green
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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2
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Bensberg M, Reiher M. Uncertainty-Aware First-Principles Exploration of Chemical Reaction Networks. J Phys Chem A 2024. [PMID: 38787736 DOI: 10.1021/acs.jpca.3c08386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
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.
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Affiliation(s)
- Moritz Bensberg
- Department of Chemistry and Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Markus Reiher
- Department of Chemistry and Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
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3
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Johnson MS, Mueller JN, Daniels C, Najm HN, Zádor J. Diffusion-Limited Kinetics in Reactive Systems. J Phys Chem A 2024. [PMID: 38670062 DOI: 10.1021/acs.jpca.4c00727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2024]
Abstract
A proper representation of chemical kinetics is vital to understanding, modeling, and optimizing many important chemical processes. In liquid and surface phases, where diffusion is slow, the rate at which the reactants diffuse together limits the overall rate of many elementary reactions. Commonly, the textbook Smoluchowski theory is utilized to estimate effective rate coefficients in the liquid phase. On surfaces, modelers commonly resort to much more complex and expensive Kinetic Monte Carlo (KMC) simulations. Here, we extend the Smoluchowski model to allow the diffusing species to undergo chemical reactions and derive analytical formulas for the diffusion-limited rate coefficients for 3D, 2D, and 2D/3D interface cases. With these equations, we are able to demonstrate that when species react faster than they diffuse they can react orders of magnitude faster than predicted by Smoluchowski theory, through what we term "the reactive transport effect". We validate the derived steady-state equations against particle Monte Carlo (PMC) simulations, KMC simulations, and non-steady-state solutions. Furthermore, using PMC and KMC simulations, we propose corrections that agree with all limits and the computed data for the 2D and 2D/3D interface steady-state equations, accounting for unique limitations in the associated derived equations. Additionally, we derive equations to handle couplings between diffusion-limited rate coefficients in reaction networks. We believe these equations should make it possible to run much more accurate mean-field simulations of liquids, surfaces, and liquid-surface interfaces accounting for diffusion limitations and the reactive transport effect.
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Affiliation(s)
- Matthew S Johnson
- Combustion Research Facility, Sandia National Laboratories, Livermore, California 94551-0969, United States
| | - Joy N Mueller
- Combustion Research Facility, Sandia National Laboratories, Livermore, California 94551-0969, United States
| | - Craig Daniels
- Combustion Research Facility, Sandia National Laboratories, Livermore, California 94551-0969, United States
| | - Habib N Najm
- Combustion Research Facility, Sandia National Laboratories, Livermore, California 94551-0969, United States
| | - Judit Zádor
- Combustion Research Facility, Sandia National Laboratories, Livermore, California 94551-0969, United States
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4
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Pang HW, Dong X, Johnson MS, Green WH. Subgraph Isomorphic Decision Tree to Predict Radical Thermochemistry with Bounded Uncertainty Estimation. J Phys Chem A 2024; 128:2891-2907. [PMID: 38536892 DOI: 10.1021/acs.jpca.4c00569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
Detailed chemical kinetic models offer valuable mechanistic insights into industrial applications. Automatic generation of reliable kinetic models requires fast and accurate radical thermochemistry estimation. Kineticists often prefer hydrogen bond increment (HBI) corrections from a closed-shell molecule to the corresponding radical for their interpretability, physical meaning, and facilitation of error cancellation as a relative quantity. Tree estimators, used due to limited data, currently rely on expert knowledge and manual construction, posing challenges in maintenance and improvement. In this work, we extend the subgraph isomorphic decision tree (SIDT) algorithm originally developed for rate estimation to estimate HBI corrections. We introduce a physics-aware splitting criterion, explore a bounded weighted uncertainty estimation method, and evaluate aleatoric uncertainty-based and model variance reduction-based prepruning methods. Moreover, we compile a data set of thermochemical parameters for 2210 radicals involving C, O, N, and H based on quantum chemical calculations from recently published works. We leverage the collected data set to train the SIDT model. Compared to existing empirical tree estimators, the SIDT model (1) offers an automatic approach to generating and extending the tree estimator for thermochemistry, (2) has better accuracy and R2, (3) provides significantly more realistic uncertainty estimates, and (4) has a tree structure much more advantageous in descent speed. Overall, the SIDT estimator marks a great leap in kinetic modeling, offering more precise, reliable, and scalable predictions for radical thermochemistry.
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Affiliation(s)
- Hao-Wei Pang
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Xiaorui Dong
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Matthew S Johnson
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - William H Green
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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5
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Kamarinopoulou NS, Wittreich GR, Vlachos DG. Direct HCN synthesis via plasma-assisted conversion of methane and nitrogen. SCIENCE ADVANCES 2024; 10:eadl4246. [PMID: 38552025 PMCID: PMC10980260 DOI: 10.1126/sciadv.adl4246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 02/23/2024] [Indexed: 04/01/2024]
Abstract
Hydrogen cyanide (HCN) is synthesized from ammonia (NH3) and methane (CH4) at ~1200°C over a Pt catalyst. Ammonia synthesis entails several complex, highly emitting processes. Plasma-assisted HCN synthesis directly from CH4 and nitrogen (N2) could be pivotal for on-demand HCN production. Here, we evaluate the potential of dielectric barrier discharge (DBD) N2/CH4 plasma for decentralized catalyst-free selective HCN production. We demonstrate a single-step conversion of methane and nitrogen to HCN with a 72% yield at <300°C. HCN is favored at low CH4 concentrations with ethane (C2H6) as the secondary product. We propose a first-principles microkinetic model with few electron impact reactions. The model accurately predicts primary product yields and elucidates that methyl radical (·CH3) is a common intermediate in HCN and C2H6 synthesis. Compared to current industrial processes, N2/CH4 DBD plasma can achieve minimal CO2 emissions.
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Affiliation(s)
| | - Gerhard R. Wittreich
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE, USA
| | - Dionisios G. Vlachos
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE, USA
- Delaware Energy Institute, University of Delaware, Newark, DE, USA
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6
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Chung Y, Green WH. Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates. Chem Sci 2024; 15:2410-2424. [PMID: 38362410 PMCID: PMC10866337 DOI: 10.1039/d3sc05353a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 01/04/2024] [Indexed: 02/17/2024] Open
Abstract
Fast and accurate prediction of solvent effects on reaction rates are crucial for kinetic modeling, chemical process design, and high-throughput solvent screening. Despite the recent advance in machine learning, a scarcity of reliable data has hindered the development of predictive models that are generalizable for diverse reactions and solvents. In this work, we generate a large set of data with the COSMO-RS method for over 28 000 neutral reactions and 295 solvents and train a machine learning model to predict the solvation free energy and solvation enthalpy of activation (ΔΔG‡solv, ΔΔH‡solv) for a solution phase reaction. On unseen reactions, the model achieves mean absolute errors of 0.71 and 1.03 kcal mol-1 for ΔΔG‡solv and ΔΔH‡solv, respectively, relative to the COSMO-RS calculations. The model also provides reliable predictions of relative rate constants within a factor of 4 when tested on experimental data. The presented model can provide nearly instantaneous predictions of kinetic solvent effects or relative rate constants for a broad range of neutral closed-shell or free radical reactions and solvents only based on atom-mapped reaction SMILES and solvent SMILES strings.
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Affiliation(s)
- Yunsie Chung
- Department of Chemical Engineering, Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - William H Green
- Department of Chemical Engineering, Massachusetts Institute of Technology Cambridge MA 02139 USA
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7
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Kreitz B, Lott P, Studt F, Medford AJ, Deutschmann O, Goldsmith CF. Automated Generation of Microkinetics for Heterogeneously Catalyzed Reactions Considering Correlated Uncertainties. Angew Chem Int Ed Engl 2023; 62:e202306514. [PMID: 37505449 DOI: 10.1002/anie.202306514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 07/06/2023] [Accepted: 07/26/2023] [Indexed: 07/29/2023]
Abstract
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.
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Affiliation(s)
- Bjarne Kreitz
- School of Engineering, Brown University, 184 Hope Street, Providence, RI, 02912, USA
- Institute for Chemical Technology and Polymer Chemistry, Karlsruhe Institute of Technology, Engesserstr. 20, 76128, Karlsruhe, Germany
| | - Patrick Lott
- Institute for Chemical Technology and Polymer Chemistry, Karlsruhe Institute of Technology, Engesserstr. 20, 76128, Karlsruhe, Germany
| | - Felix Studt
- Institute for Chemical Technology and Polymer Chemistry, Karlsruhe Institute of Technology, Engesserstr. 20, 76128, Karlsruhe, Germany
- Institute of Catalysis Research and Technology, Karlsruhe Institute of Technology, 76344, Eggenstein-Leopoldshafen, Germany
| | - Andrew J Medford
- School of Chemical and Biomolecular Engineering, Atlanta, GA, 30318, USA
| | - Olaf Deutschmann
- Institute for Chemical Technology and Polymer Chemistry, Karlsruhe Institute of Technology, Engesserstr. 20, 76128, Karlsruhe, Germany
| | - C Franklin Goldsmith
- School of Engineering, Brown University, 184 Hope Street, Providence, RI, 02912, USA
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8
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Johnson MS, Gierada M, Hermes ED, Bross DH, Sargsyan K, Najm HN, Zádor J. Pynta─An Automated Workflow for Calculation of Surface and Gas-Surface Kinetics. J Chem Inf Model 2023; 63:5153-5168. [PMID: 37559203 DOI: 10.1021/acs.jcim.3c00948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/11/2023]
Abstract
Many important industrial processes rely on heterogeneous catalytic systems. However, given all possible catalysts and conditions of interest, it is impractical to optimize most systems experimentally. Automatically generated microkinetic models can be used to efficiently consider many catalysts and conditions. However, these microkinetic models require accurate estimation of many thermochemical and kinetic parameters. Manually calculating these parameters is tedious and error prone, involving many interconnected computations. We present Pynta, a workflow software for automating the calculation of surface and gas-surface reactions. Pynta takes the reactants, products, and atom maps for the reactions of interest, generates sets of initial guesses for all species and saddle points, runs all optimizations, frequency, and IRC calculations, and computes the associated thermochemistry and rate coefficients. It is able to consider all unique adsorption configurations for both adsorbates and saddle points, allowing it to handle high index surfaces and bidentate species. Pynta implements a new saddle point guess generation method called harmonically forced saddle point searching (HFSP). HFSP defines harmonic potentials based on the optimized adsorbate geometries and which bonds are breaking and forming that allow initial placements to be optimized using the GFN1-xTB semiempirical method to create reliable saddle point guesses. This method is reaction class agnostic and fast, allowing Pynta to consider all possible adsorbate site placements efficiently. We demonstrate Pynta on 11 diverse reactions involving monodenate, bidentate, and gas-phase species, many distinct reaction classes, and both a low and a high index facet of Cu. Our results suggest that it is very important to consider reactions between adsorbates adsorbed in all unique configurations for interadsorbate group transfers and reactions on high index surfaces.
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Affiliation(s)
- Matthew S Johnson
- Combustion Research Facility, Sandia National Laboratories, Livermore, California 94550, United States
| | - Maciej Gierada
- Combustion Research Facility, Sandia National Laboratories, Livermore, California 94550, United States
| | - Eric D Hermes
- Combustion Research Facility, Sandia National Laboratories, Livermore, California 94550, United States
| | - David H Bross
- Chemical Sciences and Engineering Division, Argonne National Laboratory, Lemont, Illinois 60439, United States
| | - Khachik Sargsyan
- Combustion Research Facility, Sandia National Laboratories, Livermore, California 94550, United States
| | - Habib N Najm
- Combustion Research Facility, Sandia National Laboratories, Livermore, California 94550, United States
| | - Judit Zádor
- Combustion Research Facility, Sandia National Laboratories, Livermore, California 94550, United States
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9
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Kreitz B, Abeywardane K, Goldsmith CF. Linking Experimental and Ab Initio Thermochemistry of Adsorbates with a Generalized Thermochemical Hierarchy. J Chem Theory Comput 2023. [PMID: 37354113 DOI: 10.1021/acs.jctc.3c00112] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/26/2023]
Abstract
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.
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Affiliation(s)
- Bjarne Kreitz
- School of Engineering, Brown University, Providence, Rhode Island 02912, United States
| | - Kento Abeywardane
- School of Engineering, Brown University, Providence, Rhode Island 02912, United States
| | - C Franklin Goldsmith
- School of Engineering, Brown University, Providence, Rhode Island 02912, United States
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10
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Dana AG, Johnson MS, Allen JW, Sharma S, Raman S, Liu M, Gao CW, Grambow CA, Goldman MJ, Ranasinghe DS, Gillis RJ, Payne AM, Li Y, Dong X, Spiekermann KA, Wu H, Dames EE, Buras ZJ, Vandewiele NM, Yee NW, Merchant SS, Buesser B, Class CA, Goldsmith F, West RH, Green WH. Automated reaction kinetics and network exploration (Arkane): A statistical mechanics, thermodynamics, transition state theory, and master equation software. INT J CHEM KINET 2023. [DOI: 10.1002/kin.21637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
Affiliation(s)
- Alon Grinberg Dana
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
- The Wolfson Department of Chemical Engineering and Grand Technion Energy Program (GTEP) Technion – Israel Institute of Technology Haifa Israel
| | - Matthew S. Johnson
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Joshua W. Allen
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Sandeep Sharma
- Department of Chemistry University of Colorado Boulder CO USA
| | - Sumathy Raman
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Mengjie Liu
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Connie W. Gao
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Colin A. Grambow
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Mark J. Goldman
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Duminda S. Ranasinghe
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Ryan J. Gillis
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - A. Mark Payne
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Yi‐Pei Li
- Department of Chemical Engineering National Taiwan University Taipei Taiwan
| | - Xiaorui Dong
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Kevin A. Spiekermann
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Haoyang Wu
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Enoch E. Dames
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Zachary J. Buras
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Nick M. Vandewiele
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Nathan W. Yee
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Shamel S. Merchant
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Beat Buesser
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Caleb A. Class
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | | | - Richard H. West
- Department of Chemical Engineering Northeastern University Boston Massachusetts USA
| | - William H. Green
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
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11
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Concentration‐Flux‐Steered Mechanism Exploration with an Organocatalysis Application. Isr J Chem 2023. [DOI: 10.1002/ijch.202200123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
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12
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Sharma S, Abeywardane K, Goldsmith CF. Theory-Based Mechanism for Fluoromethane Combustion I: Thermochemistry and Abstraction Reactions. J Phys Chem A 2023; 127:1499-1511. [PMID: 36745864 DOI: 10.1021/acs.jpca.2c06623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
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.
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Affiliation(s)
- Siddha Sharma
- Chemical Engineering, School of Engineering, Brown University, Providence, Rhode Island 02912, United States
| | - Kento Abeywardane
- Chemical Engineering, School of Engineering, Brown University, Providence, Rhode Island 02912, United States
| | - C Franklin Goldsmith
- Chemical Engineering, School of Engineering, Brown University, Providence, Rhode Island 02912, United States
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13
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The Accuracy of Semi-Empirical Quantum Chemistry Methods on Soot Formation Simulation. Int J Mol Sci 2022; 23:ijms232113371. [DOI: 10.3390/ijms232113371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 10/29/2022] [Accepted: 10/30/2022] [Indexed: 11/06/2022] Open
Abstract
Soot molecules are hazardous compounds threatening human health. Computational chemistry provides efficient tools for studying them. However, accurate quantum chemistry calculation is costly for the simulation of large-size soot molecules and high-throughput calculations. Semi-empirical (SE) quantum chemistry methods are optional choices for balancing computational costs. In this work, we validated the performances of several widely used SE methods in the description of soot formation. Our benchmark study focuses on, but is not limited to, the validation of the performances of SE methods on reactive and non-reactive MD trajectory calculations. We also examined the accuracy of SE methods of predicting soot precursor structures and energy profiles along intrinsic reaction coordinate(s) (IRC). Finally, we discussed the spin density predicted by SE methods. The SE methods validated include AM1, PM6, PM7, GFN2-xTB, DFTB2, with or without spin-polarization, and DFTB3. We found that the shape of MD trajectory profiles, the relative energy, and molecular structures predicted by SE methods are qualitatively correct. We suggest that SE methods can be used in massive reaction soot formation event sampling and primary reaction mechanism generation. Yet, they cannot be used to provide quantitatively accurate data, such as thermodynamic and reaction kinetics ones.
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