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New Domains in Automatic Mechanism Generation NEW DOMAINS IN AUTOMATIC MECHANISM GENERATION A Dissertation Presented By Belinda Leigh Slakman To The Department of Chemical Engineering In partial fulfillment of the requirements For the degree of Doctor of Philosophy In the Field of Chemical Engineering Northeastern University Boston, Massachusetts August 2017 ii Abstract Deeper understanding of complex chemical systems can be aided by detailed kinetic mod- eling, in which processes are broken down into their individual elementary reactions. An important industrial goal is to move from postdictive to predictive modeling, where new chemical vapor deposition (CVD) precursors, for example, can be tested for efficiency without performing tedious and expensive experiments. Some of these microkinetic mod- els may contain hundreds of reacting chemical species, and thousands of reactions; thus, it is desirable to build the models automatically with a computer to speed up model gen- eration and reduce errors. Automatic mechanism generation is now commonly used for applications such as combustion, but extension to other systems presents challenges. This dissertation describes the extension of the Reaction Mechanism Generator (RMG) soft- ware to two less-studied chemical systems: the oxidation of liquid fuels and the gas-phase decomposition of silicon hydrides. To model liquid fuel oxidation, the software’s existing gas-phase thermodynamics and kinetics databases needed to be supplemented, or corrected to account for solvated re- actions. Existing correlations and data for solvation thermodynamics and diffusion were improved and added to RMG. Solvation kinetics data were obtained by developing a ma- chine learning algorithm to systematically predict the change in barrier height when going from gas-phase to various solvents. The algorithm was trained with quantum chemistry cal- culations on a simple set of hydrogen abstraction and intra-hydrogen migration reactions. The method was used to change the rates in a model for the oxidation of dodecane/methyl oleate blends, showing a marked change in the models prediction for the fuel’s induction period. iii The second part of this dissertation involves gas-phase silicon hydride decomposi- tion, for the application to CVD. Thermodynamic and kinetic data were added from liter- ature to RMG’s database. Specifically focusing on radical reaction types, additional data were calculated via quantum chemistry for hydrogen bond increment (HBI) values of sili- con hydride species, as well as hydrogen abstraction reaction rates. A SiH4 decomposition model was built with the updated RMG and compared to experiment, with good agreement. This work provides new insight on both of these chemical systems and contributes new calculated thermodynamics and kinetics parameters. Importantly, it also guides future developers in adding capabilities for new phases or elements to mechanism generation software. iv ACKNOWLEDGEMENTS My dissertation work would not have been possible without the support of many people, near and far. Thank you to my advisor, Dr. Richard West, for supporting me these past five years. Your intelligence and insights have been invaluable, but at the same time, you have always let me run with the ideas I have had and let me make mistakes on my own- the marks of a great advisor. Thanks for giving me the opportunities to travel and present my research, teach, and mentor. I also want to thank my other committee members: Dr. Anand Asthagiri, Dr. Carolyn Lee-Parsons, Dr. Mary Jo Ondrechen, and Dr. Harsono Simka for your time and helpful discussions over the years. I would especially like to thank Harsono for mentoring me throughout two internships at Intel Corporation and beyond, and for all of your personal and professional advice. I want to thank my other colleagues at Intel, particularly Karson Knutson, Dr. Har- inath Reddy, and other members of the TCAD-IPAG group. I learned a lot from all of you, and thanks for giving me the opportunity to come back and work with you a second time. This dissertation would surely not have been possible without the hard work of past and present RMG developers in the Green Group at MIT. I would especially like to thank Dr. William Green for his insights over the years, and Dr. Amrit Jalan and Yunsie Chung for helpful discussions on solvation in RMG. This work could not have been completed without the help of Research Comput- ing at Northeastern University, especially Nilay Roy, who helped maintain the Discovery cluster. v I want to thank Michael Li, and my instructors and colleagues at The Data Incuba- tor, for teaching me about data science, and providing guidance and friendship during the spring of 2017. I would also like to acknowledge Lilian Tsang and the organizers of the Combustion Energy Frontier Research Center summer school, which I attended in 2013 and 2014. I would like to thank the current and former Northeastern Department of Chemical Engineering staff for their support: Jessica, Brandon, Francesca, Kelly, Sarah and espe- cially Pat and Rob. I also have to thank my 11 classmates and friends who started this journey with me: Chris, Dan, Hunter, Luting, Mark, Negar, Oljora, Sue, Sydney, Tanya and Taylor. To Dr. Pierre Bhoorasingh and Dr. Fariba Seyedzadeh Khanshan: I couldn’t have done this work without your guidance, good example, and levity, and thank you for the friendship and advice you continue to provide. Thank you to Jason for being our “scientist” and for all of your research assistance. I’d also like to thank Nate for letting me vent, not just about kinetics. I also want to thank current West group members Yawei, Mike, Rasha and Krishna and past members Jacob, Victor, Elliot, Claudia, and Drew for your useful discussions and companionship; it’s been a pleasure to work with all of you. I have to thank all of my friends, in Boston and beyond. In particular, I want to thank Amanda for being my roommate for 4 years, Ina for sharing in the best friendship that has ever come from Craigslist (#18westwood), Katarina for reminding me that my Ph.D. project is not the only measure of my worth, and Jennifer for judgment-free support of my decisions. Last, and most importantly, I have to thank my family, who has patiently supported me for the past 28 years. Mom, Dad, Jordan and Casey, thank you for standing with me as I take this next step. I am lucky to have such love in my life. vi Contents 1 Introduction 1 1.1 Automatic mechanism generation . .2 1.1.1 Reaction Mechanism Generator (RMG) . .6 1.2 Parameter estimation . .9 1.2.1 Thermodynamics . .9 1.2.2 Kinetics . 11 1.2.3 Transition state geometries . 13 1.3 Machine learning with decision trees . 13 1.3.1 Decision trees in chemistry and biology . 14 1.4 Dissertation overview . 15 2 Automatic calculation of solvation thermodynamics 17 2.1 Background . 18 2.1.1 Explicit thermodynamic calculations . 18 2.1.2 Estimation techniques . 18 2.2 Methods . 21 2.2.1 Adaptation of solvation thermodynamics from RMG-Java . 22 2.2.2 New additions in RMG-Py . 23 2.3 Results . 27 2.3.1 Estimation of solute descriptors . 27 2.3.2 Calculation of solvation thermodynamics . 28 vii 2.4 Summary . 29 2.5 Recommendations . 29 2.5.1 Temperature dependence of solvation thermodynamics . 30 2.5.2 Calculation of solvation thermodynamics for lone pair species . 30 2.5.3 Improved benchmarking of group additivity values . 30 2.5.4 Expansion and enhancement of solvents . 31 3 Implementing kinetic solvent effects in automatic mechanism generation 32 3.1 Background . 33 3.1.1 Experimental techniques for determining reaction rates in liquids . 34 3.1.2 Computational chemistry . 35 3.1.3 Kinetic solvent effects within reaction families . 38 3.2 Methods . 49 3.2.1 Diffusion . 49 3.2.2 Intrinsic kinetics . 50 3.2.3 Fuel oxidation model modification . 54 3.2.4 Reactor simulations . 54 3.3 Results . 55 3.3.1 Solvation kinetics trends . 55 3.3.2 New reactor simulations . 58 3.4 Summary . 58 3.5 Recommendations . 61 3.5.1 On-the-fly estimation of solvation kinetics . 61 3.5.2 Benchmarking the estimates . 62 3.5.3 Check thermodynamic consistency with LSERs . 62 3.5.4 Data-driven approaches . 63 viii 4 Automated silicon hydride mechanism generation 66 4.1 Background . 67 4.1.1 Experimental work on SiH4 chemistry . 67 4.1.2 Detailed mechanisms for SiH4 CVD . 68 4.1.3 Importance of radical chemistry in silicon hydride thermal decom- position . 70 4.2 Methods . 70 4.2.1 RMG source code . 71 4.2.2 Updating RMG’s database . 71 4.2.3 RMG model generation . 74 4.2.4 Reactor modeling . 74 4.3 Results . 75 4.3.1 Kinetics of hydrogen abstraction reactions . 75 4.3.2 Calculated thermodynamic data . 76 4.3.3 RMG generated mechanisms . 79 4.4 Discussion . 86 4.5 Summary . 87 4.6 Recommendations . 88 4.6.1 Expansion of thermodynamic libraries for radical species . 88 4.6.2 Calculation of rates . 89 4.6.3 Sensitivity analysis . 90 4.6.4 Surface chemistry . 91 5 Conclusion 92 5.1 Liquid-phase fuel oxidation . 92 5.2 Thermal decomposition of silicon hydrides . 94 5.3 Summary . 95 ix References 96 A Supplementary Info for Solvation Kinetics 114 A.1 Solvation kinetics molecular structure group trees and values . 114 A.2 Script for modifying Chemkin files for solvation kinetics corrections . 116 A.3 Modified Cantera input file for n-dodecane/ methyl oleate oxidation . 116 A.4 Cantera script to simulate liquid fuel oxidation reactor . 116 A.5 Code for automatic tree building . 116 B Supplementary Info for Silicon Hydrides 117 B.1 Geometries of reactants and transition states for hydrogen abstraction reac- tions .
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