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Meeting MS&T22: Materials Science & Technology
Symposium Ceramics and Glasses Modeling by Simulations and Machine Learning
Presentation Title Predicting and Accessing Metastable Phases
Author(s) Vancho Kocevski, James A. Valdez, Benjamin K. Derby, Ghanshyam Pilania, Blas P. Uberuaga
On-Site Speaker (Planned) Vancho Kocevski
Abstract Scope Metastable phases can have distinct properties compared to the ground state, increasing the usability of the materials in technological applications. The applicability of lanthanide sesquioxides (Ln2O3) in solid-oxide fuel cells and as irradiation resistant materials have been attributed to the relative ease with which they transform to different polymorphs. An efficient method to estimate the amount of stored energy that different metastable phases require by irradiation for their practical realization can aid in understanding and rationalizing their irradiation response. Here, we calculate metastable phase diagrams, from which we extract the metastability threshold – the excess energy stored in the metastable phase relative to the ground state. We demonstrate how metastable phase diagrams provide new insight into the synthesis and irradiation behavior of Ln2O3. We successfully predict the sequence of metastable phase formation of Lu2O3 irradiated at cryogenic temperature, forming three metastable phases with increasing irradiation fluence, displaying unique irradiation behavior.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Physics Informed Machine Learning Approach to Predict Glass Forming Ability
D-7: Development of Structural Descriptors to Predict Dissolution Rate of Volcanic Glasses: Molecular Dynamic Simulations
D-8: Molecular Dynamic Simulations of Polymer Derived Ceramics
Data-driven Prediction of Room Temperature Density of Multicomponent Silicate-based Glasses
Data Driven Design and Enhancement of Machinable Glass Ceramics
Developing ReaxFF for Simulation of Silicon Carbonitride Polymer-derived Ceramics
In-Silico Simulations of Polymer Pyrolysis
Machine Learning-Derived Atomistic Potentials for Y2Si2O7 and Yb2Si2O7
Machine Learning Defect Properties of Semiconductors
Machine Learning to Design and Discover Sustainable Cementitious Binders: Learning from Small Databases and Developing Closed-form Analytical Models
Molecular Dynamics Simulation of Tellurite Glasses
Molecular Dynamics Study of Domain Switching Dynamics in KNbO3 and BaTiO3
Natural Language Processing Aided Understanding of Material Science Literature
Pore-resolved Simulations of Chemical Vapor Infiltration in 3D Printed Preforms and the Kinetic Regimes
Predicting and Accessing Metastable Phases
Predicting the Dynamics of Atoms in Glass-Forming Liquids by a Surrogate Machine-Learned Simulator
Quantifying the Local Structure of Metallic Glass as a Function of Composition and Atomic Size
Using Machine Learning Empirical Potentials to Investigate Interdiffusion at Metal-Chalcogenide Alloy Interfaces

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