Investigation of Atomic Environments by Computational Thermodynamics: Applications in Intermetallic Catalysts and Molten Salts

Open Access
- Author:
- Gong, Rushi
- Graduate Program:
- Materials Science and Engineering
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- August 27, 2024
- Committee Members:
- John Mauro, Program Head/Chair
Zi-Kui Liu, Chair & Dissertation Advisor
Hojong Kim, Major Field Member
Michael Janik, Outside Unit & Field Member
John Mauro, Major Field Member - Keywords:
- CALPHAD
Thermodynamics
First-principles calculations
Catalysts
Intermetallics
Molten salts
Short-range ordering
Software - Abstract:
- Understanding atomic environments is essential for optimizing the performance of materials by providing insights into underlying structure-property relationships. For complex multi-component solution phases, advanced thermodynamic models are required to capture the inherent complexities of atomic environments in the phase, including both long-range and short-range ordering. CALPHAD modeling, which describes the Gibbs energies of individual phases, is a key tool for investigating multi-component thermodynamics. One of the challenges in CALPHAD modeling is comparing various thermodynamic models and selecting the most appropriate one for each phase. Open-source software tools, PyCalphad and ESPEI, have enabled high-throughput CALPHAD modeling with uncertainty quantification. The integration of Bayesian parameter estimation and the Markov Chain Monte Carlo approach into ESPEI facilitates Bayesian model selection in CALPHAD modeling, enabling systematic identification of the most appropriate thermodynamic models for phase description. This dissertation discusses the selection and statistical comparison of thermodynamic models for describing atomic environments in individual phases, focusing on intermetallic catalysts and molten salts. Employing a four-sublattice model for the Pd-Zn-based γ-brass phase successfully predicts the site occupancy of active metals within intermetallic structures. This atomic control of active-site ensembles in Pd-Zn-based γ-brass intermetallic catalysts improves activity and selectivity for hydrogenation reactions. For complex molten salts, candidate models such as the associate model, two-sublattice ionic model, and modified quasichemical model with quadruplets approximation are considered to describe the short-range ordering. Bayesian statistics are applied to compare these models and identify the most favorable one for molten salt liquids, further enhancing the accuracy of thermodynamic modeling and salt properties predictions. Additionally, a template generator has been developed in the present dissertation to allow users to add customized thermodynamic models within PyCalphad. These advancements provide the community with new advanced opportunities to comprehensively evaluate thermodynamic modeling with uncertainty quantification and model selection, accelerating the optimization process in data-driven materials design and discovery.