Jiadong Chen: High dimensional phase diagrams: Engineering relative stability in 4-dimensions

Sequential learning algorithms based on Bayesian optimization are routinely being deployed for materials stability optimization in high-parameter spaces. We anticipate these optimization methods would perform better if they were built upon stronger priors, for example, as derived from the fundamental thermodynamics underlying the equilibrium behavior of materials. Here, we present a thermodynamics-based technique to optimize the relative stability of a materials in high-dimensional thermodynamic space, based on a new derivation of a generalized high-dimensional Clausius Clapeyron relation. Using this thermodynamic infrastructure, we design several pathways to enhance the relative acid stability of Mn-oxides versus its dissolved states for potential electrochemical catalyst application. We construct a 4-D Pourbaix diagram with pH, redox potential E, particle radius 1/R and a chemical potential μK as axis. By exploring the gradients of the high-dimensional phase boundaries, we derive first-principles insights that nano-sizing (1/R) and certain doping ions (μK) can stabilize some metastable Mn-oxides polymorphs, where 1/R decreases acid stability and μK increases it. Our high-dimensional thermodynamic framework is a general method to engineer relative stability in parameter spaces that leverage multiple forms of thermodynamic work.

Bio:
Jiadong Chen is a 5th year PhD in materials science and engineering department, Wenhao Sun group, focusing on use computational and data-driven methods to predict materials stability and synthesis recipes.

Jeffrey Hatch will present his talk: Computational Methods in Chemistry.

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