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Chemical Reactions and Thermodynamic...

Kussainova, Dina,

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Chemical Reactions and Thermodynamics of Molten Salts Using Ab Initio-Based Machine Learning Models /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Chemical Reactions and Thermodynamics of Molten Salts Using Ab Initio-Based Machine Learning Models // Dina Kussainova.
Author:	Kussainova, Dina,
Description:	1 electronic resource (150 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
Contained By:	Dissertations Abstracts International86-12B.
Subject:	Chemical engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=32038650
ISBN:	9798280748163

Chemical Reactions and Thermodynamics of Molten Salts Using Ab Initio-Based Machine Learning Models /
Kussainova, Dina,

Chemical Reactions and Thermodynamics of Molten Salts Using Ab Initio-Based Machine Learning Models /Dina Kussainova. - 1 electronic resource (150 pages)

Source: Dissertations Abstracts International, Volume: 86-12, Section: B.

Understanding the chemical reactions and thermodynamic properties of molten salts and their aqueous solutions is crucial for designing their technological and environmental applications in the energy industry. While quantum chemical methods can describe these systems accurately, their high computational cost limits the size of systems and time scales. Machine learning models trained on ab initio data have been applied in large-scale molecular dynamics simulations, achieving similar accuracy with greater efficiency. In this dissertation, machine learning models have been developed to model the chemical reactions in lithium carbonates (Li2CO3) and hydroxides (LiOH) at elevated temperatures. Initially, a machine learning model was generated for Li2CO3 using ab initio data for both vapor and liquid phases. Reactive vapor-liquid equilibria of Li2CO3 were studied using direct coexistence simulations, and the observed partial pressure of CO2 closely matched experiments. After demonstrating that machine learning-based simulations can efficiently describe reactive multiphase behavior, the same methodology was applied to LiOH. The new model revealed that decomposition produces H2O dissolved in the liquid phase, with some vaporized in the presence of the vapor phase. Comparison with the underlying ab initio method showed good agreement in terms of reaction kinetics and equilibrium liquid properties. Additionally, a general model for both Li2CO3 and LiOH was trained using data from the previous models as well as new data on mixtures. This model was used to study decomposition reactions in pure and mixed systems and showed that LiOH can react with CO2 producing Li2CO3 and H2O. After demonstrating the ability of machine learning models to accurately predict thermodynamic and kinetic properties of fluids, a new model was generated for aqueous NaCl solutions to perform more challenging calculations - chemical potentials. Different methods were evaluatedand modified to calculate chemical potentials of alkali halides in water with machine learning potentials. The studies in this dissertation demonstrate how a machine learning model can be developed to study reactive multiphase equilibria of molten salts, combining the accuracy of ab initio methods with the efficiency of classical molecular dynamics. The methodology described can be extended to other fluids and materials, particularly those with limitedexperimental data.

English

ISBN: 9798280748163Subjects--Topical Terms:

560457
Chemical engineering.
Subjects--Index Terms:

Lithium carbonates

Chemical Reactions and Thermodynamics of Molten Salts Using Ab Initio-Based Machine Learning Models /
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520 $a Understanding the chemical reactions and thermodynamic properties of molten salts and their aqueous solutions is crucial for designing their technological and environmental applications in the energy industry. While quantum chemical methods can describe these systems accurately, their high computational cost limits the size of systems and time scales. Machine learning models trained on ab initio data have been applied in large-scale molecular dynamics simulations, achieving similar accuracy with greater efficiency. In this dissertation, machine learning models have been developed to model the chemical reactions in lithium carbonates (Li2CO3) and hydroxides (LiOH) at elevated temperatures. Initially, a machine learning model was generated for Li2CO3 using ab initio data for both vapor and liquid phases. Reactive vapor-liquid equilibria of Li2CO3 were studied using direct coexistence simulations, and the observed partial pressure of CO2 closely matched experiments. After demonstrating that machine learning-based simulations can efficiently describe reactive multiphase behavior, the same methodology was applied to LiOH. The new model revealed that decomposition produces H2O dissolved in the liquid phase, with some vaporized in the presence of the vapor phase. Comparison with the underlying ab initio method showed good agreement in terms of reaction kinetics and equilibrium liquid properties. Additionally, a general model for both Li2CO3 and LiOH was trained using data from the previous models as well as new data on mixtures. This model was used to study decomposition reactions in pure and mixed systems and showed that LiOH can react with CO2 producing Li2CO3 and H2O. After demonstrating the ability of machine learning models to accurately predict thermodynamic and kinetic properties of fluids, a new model was generated for aqueous NaCl solutions to perform more challenging calculations - chemical potentials. Different methods were evaluatedand modified to calculate chemical potentials of alkali halides in water with machine learning potentials. The studies in this dissertation demonstrate how a machine learning model can be developed to study reactive multiphase equilibria of molten salts, combining the accuracy of ab initio methods with the efficiency of classical molecular dynamics. The methodology described can be extended to other fluids and materials, particularly those with limitedexperimental data.
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650 4 $a Computational chemistry. $3 3350019
650 4 $a Materials science. $3 543314
650 4 $a Engineering. $3 586835
653 $a Lithium carbonates
653 $a Lithium hydroxides
653 $a Machine learning models
653 $a Molecular dynamics
653 $a Molten salts
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710 2 $a Princeton University. $b Chemical and Biological Engineering. $e degree granting institution. $3 3816384
720 1 $a Panagiotopoulos, Athanassios Z. $e degree supervisor.
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791 $a Ph.D.
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