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Meeting 2023 TMS Annual Meeting & Exhibition
Symposium Computational Discovery and Design of Materials
Presentation Title Developing an Ab Initio-Kinetic Passivation Model for High-throughput Screening of Material Stability
Author(s) Rachel Gorelik, Arunima K. Singh
On-Site Speaker (Planned) Rachel Gorelik
Abstract Scope With corrosion remaining a significant economic issue, the ability to a priori predict the kinetics of material corrosion remains an important consideration in the field of materials discovery, which often lacks the ability to predict time-dependent corrosion behavior during high-throughput screening. To address this challenge, we have developed an ab initio-kinetic framework for predicting material stability by combining density functional theory and molecular dynamics simulations with a kinetic passivation model called the Point Defect Model (PDM). This non-empirical framework can predict the growth rate of passivation films in any elemental material without prior experimental knowledge. After developing and automating this workflow, we have evaluated its performance through two common metal case studies (Cu and W) and compared with available experimental literature. Finally, we have evaluated the viability of extending this framework to the more than 700 elemental materials which are currently available in the Materials Project database.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, Other, Other

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Applying Data-driven Models in Materials Science: Unraveling Hidden Relationships between Structures and Properties
Atomistic Modeling of Electronic Transport and Electrochemistry
Band Gap Renormalization in 2D Materials from First-principles
Bridging First-principles Calculations with Experiment: Insights from Case Studies on (Photo)Electrochemical Systems
Closed Loop Computational Materials Discovery
Computation Discovery of Materials for Solid-state Batteries
Computational Design for Metallic Meso-architected Materials for Dynamics
Computer Vision Problems in Transmission Electron Microscopy
Crystal to PNG (xtal2png): A Screening Tool to Accelerate Domain Transfer from State-of-the-art Image-processing Models to Materials Informatics and a Case Study on Denoising Diffusion Probabilistic Models
Data- and Physics-driven Approaches to Discovering the Governing Equations for Complex Phenomena in Heterogeneous Materials
Design and Development of High Strength High Conductivity Alloys using ICMDŽ Approach
Design of Bistable Metamaterials for Desired Dynamic Behavior
Designing High-Tc Superconductors with BCS-inspired Screening, Density Functional Theory and Deep-learning
Designing Ohmic and Schottky Interfaces for Oxide Electronics
Developing an Ab Initio-Kinetic Passivation Model for High-throughput Screening of Material Stability
Electronic and Structural Properties of Ab-initio Predicted BxAl1-xN Alloy Structures
Elucidating the Mechanisms for Fast Diffusion in Doped LLZO
Exploiting First-principles Based Interpretation of X-ray Absorption Spectra of Ni, Cr, Fe Elements in Molten-salt System
Graph Mining in Materials Science for the Prediction of Material Properties
M-16: Building an ImageNet for Materials Grain Boundaries
M-17: Generative Adversarial Networks and Diffusion Models in Material Discovery
M-28: Molecular Dynamics Investigation of Electrochemical Systems
Machine-learning-boosted Searching and Optimization of Emergent Quantum Materials
Machine Learning Assisted Discovery of Composite Solid-state Electrolytes in Context of Li-ion Batteries
Modeling of Local Lattice Distortion Effects on Vacancy Migrations in Multicomponent FCC Alloys
Searching for New "Quantum Defects" through High-throughput Computational Screening
Ultra-fast Interpretable Machine-learning Potentials for Accelerated Structure Prediction of Materials
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