ProgramMaster Logo
Conference Tools for 2023 TMS Annual Meeting & Exhibition
Login
Register as a New User
Help
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools
About this Abstract
Meeting 2023 TMS Annual Meeting & Exhibition
Symposium Computational Discovery and Design of Materials
Presentation Title Exploiting First-principles Based Interpretation of X-ray Absorption Spectra of Ni, Cr, Fe Elements in Molten-salt System
Author(s) Mehmet Topsakal, Kaifeng Zheng, Nirmalendu Patra, Michael Woods, Ruchi Gakhar, Phillip Halstenberg, Shannon Mark Mahurin, Anatoly Frenkel, Simerjeet Gill
On-Site Speaker (Planned) Mehmet Topsakal
Abstract Scope First-principles simulations of XAS can play a critical role in interpreting the abstract spectral features as observed in XAS spectra and draw physical insights into local s­tructure. For example, understanding the effect of solvent chemistry and radiation on the speciation and local structure of metals is crucial for predicting the stability and reactivity of molten salts for successful deployment of molten salt reactor (MSR) systems. Due to its element-specific feature, XAS can allow studying metal species' local coordination environment and chemical structure in molten salt systems. In this work, we demonstrate the interpretation of experimentally observed XAS via first-principles simulations on model molten salt systems containing Ni, Cr, and Fe. Multiple approaches will be benchmarked and compared to elucidate benefits and limitations. This work was supported as part of the Molten Salts in Extreme Environments, Energy Frontier Research Center, funded by the U.S. Department of Energy Office of Science.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, Nuclear Materials, Modeling and Simulation

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Adaptive Discovery and Mixed-variable Bayesian Optimization of Next Generation Synthesizable Microelectronic Materials
An Inverse Materials Design Route Based on Structure-property Linkages Leveraging 3D Convolutional Neural Network and Bayesian Optimization
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
What is a Minimal Working Example for a Self-driving Laboratory?
Questions about ProgramMaster? Contact programming@programmaster.org