The Experts below are selected from a list of 82545 Experts worldwide ranked by ideXlab platform
Luke F Roberts - One of the best experts on this subject based on the ideXlab platform.
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skynet a modular nuclear Reaction Network library
Astrophysical Journal Supplement Series, 2017Co-Authors: Jonas Lippuner, Luke F RobertsAbstract:Almost all of the elements heavier than hydrogen that are present in our solar system were produced by nuclear burning processes either in the early universe or at some point in the life cycle of stars. In all of these environments, there are dozens to thousands of nuclear species that interact with each other to produce successively heavier elements. In this paper, we present SkyNet, a new general-purpose nuclear Reaction Network that evolves the abundances of nuclear species under the influence of nuclear Reactions. SkyNet can be used to compute the nucleosynthesis evolution in all astrophysical scenarios where nucleosynthesis occurs. SkyNet is free and open source, and aims to be easy to use and flexible. Any list of isotopes can be evolved, and SkyNet supports different types of nuclear Reactions. SkyNet is modular so that new or existing physics, like nuclear Reactions or equations of state, can easily be added or modified. Here, we present in detail the physics implemented in SkyNet with a focus on a self-consistent transition to and from nuclear statistical equilibrium to non-equilibrium nuclear burning, our implementation of electron screening, and coupling of the Network to an equation of state. We also present comprehensive code tests and comparisons with existing nuclear Reaction Networks. We find that SkyNet agrees with published results and other codes to an accuracy of a few percent. Discrepancies, where they exist, can be traced to differences in the physics implementations.
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skynet a modular nuclear Reaction Network library
Astrophysical Journal Supplement Series, 2017Co-Authors: Jonas Lippuner, Luke F RobertsAbstract:Almost all of the elements heavier than hydrogen that are present in our solar system were produced by nuclear burning processes either in the early universe or at some point in the life cycle of stars. In all of these environments, there are dozens to thousands of nuclear species that interact with each other to produce successively heavier elements. In this paper, we present SkyNet, a new general-purpose nuclear Reaction Network that evolves the abundances of nuclear species under the influence of nuclear Reactions. SkyNet can be used to compute the nucleosynthesis evolution in all astrophysical scenarios where nucleosynthesis occurs. SkyNet is free and open-source and aims to be easy to use and flexible. Any list of isotopes can be evolved and SkyNet supports various different types of nuclear Reactions. SkyNet is modular so that new or existing physics, like nuclear Reactions or equations of state, can easily be added or modified. Here, we present in detail the physics implemented in SkyNet with a focus on a self-consistent transition to and from nuclear statistical equilibrium (NSE) to non-equilibrium nuclear burning, our implementation of electron screening, and coupling of the Network to an equation of state. We also present comprehensive code tests and comparisons with existing nuclear Reaction Networks. We find that SkyNet agrees with published results and other codes to an accuracy of a few percent. Discrepancies, where they exist, can be traced to differences in the physics implementations.
Jay H Lee - One of the best experts on this subject based on the ideXlab platform.
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regularized maximum likelihood estimation of sparse stochastic monomolecular biochemical Reaction Networks
Computers & Chemical Engineering, 2016Co-Authors: Hong Jang, Kwangki K Kim, Richard D Braatz, Bhushan R Gopaluni, Jay H LeeAbstract:Abstract A sparse parameter matrix estimation method is proposed for identifying a stochastic monomolecular biochemical Reaction Network system. Identification of a Reaction Network can be achieved by estimating a sparse parameter matrix containing the Reaction Network structure and kinetics information. Stochastic dynamics of a biochemical Reaction Network system is usually modeled by a chemical master equation (CME) describing the time evolution of probability distributions for all possible states. This paper considers closed monomolecular Reaction systems for which an exact analytical solution of the corresponding chemical master equation can be derived. The estimation method presented in this paper incorporates the closed-form solution into a regularized maximum likelihood estimation (MLE) for which model complexity is penalized. A simulation result is provided to verify performance improvement of regularized MLE over least-square estimation (LSE), which is based on a deterministic mass-average model, in the case of a small population size.
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regularized maximum likelihood estimation of sparse stochastic monomolecular biochemical Reaction Networks
IFAC Proceedings Volumes, 2014Co-Authors: Ifac Fellow, Hong Jang, Kwangki K Kim, Jay H Lee, Richard D BraatzAbstract:Abstract A sparse parameter estimation method is proposed for identifying a stochastic monomolecular biochemical Reaction Network system. Identification of a Reaction Network can be achieved by estimating a sparse parameter matrix containing the Reaction Network structure and kinetics information. Stochastic dynamics of a biochemical Reaction Network system is usually modeled by a chemical master equation, which is composed of several ordinary differential equations describing the time evolution of probability distributions for all possible states. This paper considers closed monomolecular Reaction systems for which an exact analytical solution of the corresponding chemical master equation is available. The estimation method presented in this paper incorporates the closed-form solution into a regularized maximum likelihood estimation (MLE) for which model complexity is penalized, whereas most of existing studies on sparse Reaction Network identification use deterministic models for regularized least-square estimation. A simulation result is provided to verify performance improvement of the presented regularized MLE over the least squares (LSE) based on a deterministic mass-average model in the case of a small population size. Improved Reaction structure detection is achieved by adding a penalty term for l 1 regularization to the exact maximum likelihood function.
Jonas Lippuner - One of the best experts on this subject based on the ideXlab platform.
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skynet a modular nuclear Reaction Network library
Astrophysical Journal Supplement Series, 2017Co-Authors: Jonas Lippuner, Luke F RobertsAbstract:Almost all of the elements heavier than hydrogen that are present in our solar system were produced by nuclear burning processes either in the early universe or at some point in the life cycle of stars. In all of these environments, there are dozens to thousands of nuclear species that interact with each other to produce successively heavier elements. In this paper, we present SkyNet, a new general-purpose nuclear Reaction Network that evolves the abundances of nuclear species under the influence of nuclear Reactions. SkyNet can be used to compute the nucleosynthesis evolution in all astrophysical scenarios where nucleosynthesis occurs. SkyNet is free and open source, and aims to be easy to use and flexible. Any list of isotopes can be evolved, and SkyNet supports different types of nuclear Reactions. SkyNet is modular so that new or existing physics, like nuclear Reactions or equations of state, can easily be added or modified. Here, we present in detail the physics implemented in SkyNet with a focus on a self-consistent transition to and from nuclear statistical equilibrium to non-equilibrium nuclear burning, our implementation of electron screening, and coupling of the Network to an equation of state. We also present comprehensive code tests and comparisons with existing nuclear Reaction Networks. We find that SkyNet agrees with published results and other codes to an accuracy of a few percent. Discrepancies, where they exist, can be traced to differences in the physics implementations.
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skynet a modular nuclear Reaction Network library
Astrophysical Journal Supplement Series, 2017Co-Authors: Jonas Lippuner, Luke F RobertsAbstract:Almost all of the elements heavier than hydrogen that are present in our solar system were produced by nuclear burning processes either in the early universe or at some point in the life cycle of stars. In all of these environments, there are dozens to thousands of nuclear species that interact with each other to produce successively heavier elements. In this paper, we present SkyNet, a new general-purpose nuclear Reaction Network that evolves the abundances of nuclear species under the influence of nuclear Reactions. SkyNet can be used to compute the nucleosynthesis evolution in all astrophysical scenarios where nucleosynthesis occurs. SkyNet is free and open-source and aims to be easy to use and flexible. Any list of isotopes can be evolved and SkyNet supports various different types of nuclear Reactions. SkyNet is modular so that new or existing physics, like nuclear Reactions or equations of state, can easily be added or modified. Here, we present in detail the physics implemented in SkyNet with a focus on a self-consistent transition to and from nuclear statistical equilibrium (NSE) to non-equilibrium nuclear burning, our implementation of electron screening, and coupling of the Network to an equation of state. We also present comprehensive code tests and comparisons with existing nuclear Reaction Networks. We find that SkyNet agrees with published results and other codes to an accuracy of a few percent. Discrepancies, where they exist, can be traced to differences in the physics implementations.
Kristin A. Persson - One of the best experts on this subject based on the ideXlab platform.
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data driven prediction of formation mechanisms of lithium ethylene monocarbonate with an automated Reaction Network
Journal of the American Chemical Society, 2021Co-Authors: Xiaowei Xie, Mingjian Wen, Hetal D. Patel, Samuel M. Blau, Kristin A. Persson, Evan Walter Clark SpottesmithAbstract:Interfacial Reactions are notoriously difficult to characterize, and robust prediction of the chemical evolution and associated functionality of the resulting surface film is one of the grand challenges of materials chemistry. The solid-electrolyte interphase (SEI), critical to Li-ion batteries (LIBs), exemplifies such a surface film, and despite decades of work, considerable controversy remains regarding the major components of the SEI as well as their formation mechanisms. Here we use a Reaction Network to investigate whether lithium ethylene monocarbonate (LEMC) or lithium ethylene dicarbonate (LEDC) is the major organic component of the LIB SEI. Our data-driven, automated methodology is based on a systematic generation of relevant species using a general fragmentation/recombination procedure which provides the basis for a vast thermodynamic Reaction landscape, calculated with density functional theory. The shortest pathfinding algorithms are employed to explore the Reaction landscape and obtain previously proposed formation mechanisms of LEMC as well as several new Reaction pathways and intermediates. For example, we identify two novel LEMC formation mechanisms: one which involves LiH generation and another that involves breaking the (CH2)O-C(═O)OLi bond in LEDC. Most importantly, we find that all identified paths, which are also kinetically favorable under the explored conditions, require water as a reactant. This condition severely limits the amount of LEMC that can form, as compared with LEDC, a conclusion that has direct impact on the SEI formation in Li-ion energy storage systems. Finally, the data-driven framework presented here is generally applicable to any electrochemical system and expected to improve our understanding of surface passivation.
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Data-Driven Prediction of Formation Mechanisms of Lithium Ethylene Monocarbonate with an Automated Reaction Network
'American Chemical Society (ACS)', 2021Co-Authors: Xiaowei Xie, Evan Walter Clark Spotte-smith, Mingjian Wen, Hetal D. Patel, Samuel M. Blau, Kristin A. PerssonAbstract:Interfacial Reactions are notoriously difficult to characterize, and robust prediction of the chemical evolution and associated functionality of the resulting surface film is one of the grand challenges of materials chemistry. The solid–electrolyte interphase (SEI), critical to Li-ion batteries (LIBs), exemplifies such a surface film, and despite decades of work, considerable controversy remains regarding the major components of the SEI as well as their formation mechanisms. Here we use a Reaction Network to investigate whether lithium ethylene monocarbonate (LEMC) or lithium ethylene dicarbonate (LEDC) is the major organic component of the LIB SEI. Our data-driven, automated methodology is based on a systematic generation of relevant species using a general fragmentation/recombination procedure which provides the basis for a vast thermodynamic Reaction landscape, calculated with density functional theory. The shortest pathfinding algorithms are employed to explore the Reaction landscape and obtain previously proposed formation mechanisms of LEMC as well as several new Reaction pathways and intermediates. For example, we identify two novel LEMC formation mechanisms: one which involves LiH generation and another that involves breaking the (CH2)O–C(O)OLi bond in LEDC. Most importantly, we find that all identified paths, which are also kinetically favorable under the explored conditions, require water as a reactant. This condition severely limits the amount of LEMC that can form, as compared with LEDC, a conclusion that has direct impact on the SEI formation in Li-ion energy storage systems. Finally, the data-driven framework presented here is generally applicable to any electrochemical system and expected to improve our understanding of surface passivation
Hong Jang - One of the best experts on this subject based on the ideXlab platform.
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regularized maximum likelihood estimation of sparse stochastic monomolecular biochemical Reaction Networks
Computers & Chemical Engineering, 2016Co-Authors: Hong Jang, Kwangki K Kim, Richard D Braatz, Bhushan R Gopaluni, Jay H LeeAbstract:Abstract A sparse parameter matrix estimation method is proposed for identifying a stochastic monomolecular biochemical Reaction Network system. Identification of a Reaction Network can be achieved by estimating a sparse parameter matrix containing the Reaction Network structure and kinetics information. Stochastic dynamics of a biochemical Reaction Network system is usually modeled by a chemical master equation (CME) describing the time evolution of probability distributions for all possible states. This paper considers closed monomolecular Reaction systems for which an exact analytical solution of the corresponding chemical master equation can be derived. The estimation method presented in this paper incorporates the closed-form solution into a regularized maximum likelihood estimation (MLE) for which model complexity is penalized. A simulation result is provided to verify performance improvement of regularized MLE over least-square estimation (LSE), which is based on a deterministic mass-average model, in the case of a small population size.
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regularized maximum likelihood estimation of sparse stochastic monomolecular biochemical Reaction Networks
IFAC Proceedings Volumes, 2014Co-Authors: Ifac Fellow, Hong Jang, Kwangki K Kim, Jay H Lee, Richard D BraatzAbstract:Abstract A sparse parameter estimation method is proposed for identifying a stochastic monomolecular biochemical Reaction Network system. Identification of a Reaction Network can be achieved by estimating a sparse parameter matrix containing the Reaction Network structure and kinetics information. Stochastic dynamics of a biochemical Reaction Network system is usually modeled by a chemical master equation, which is composed of several ordinary differential equations describing the time evolution of probability distributions for all possible states. This paper considers closed monomolecular Reaction systems for which an exact analytical solution of the corresponding chemical master equation is available. The estimation method presented in this paper incorporates the closed-form solution into a regularized maximum likelihood estimation (MLE) for which model complexity is penalized, whereas most of existing studies on sparse Reaction Network identification use deterministic models for regularized least-square estimation. A simulation result is provided to verify performance improvement of the presented regularized MLE over the least squares (LSE) based on a deterministic mass-average model in the case of a small population size. Improved Reaction structure detection is achieved by adding a penalty term for l 1 regularization to the exact maximum likelihood function.
