Conference Logo ProgramMaster Logo
Conference Tools for 2027 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 2027 TMS Annual Meeting & Exhibition
Symposium Computational Discovery and Design of Materials
Presentation Title Enhancing of the Thermal Conductivity in Refractory-based High Entropy Alloys via Mott s-d Scattering Mechanism
Author(s) Jatinder Singh, Duc Nguyen-Manh, Jesper Byggmastar, Javier Dominguez, Pedro Ferreiros, Alessandro Mottura, Biao Cai
On-Site Speaker (Planned) Duc Nguyen-Manh
Abstract Scope The thermal conductivity of some high-entropy alloys (HEAs) based transition metals has shown to potentially increase with temperature. Refractory-based HEAs containing W, Ta, V and Cr are of great interest for fusion energy applications due to their high adaptivity, allowing to design new materials that have reduced neutron activity, and strong resistance to radiation damage. The Mott model for transition metals theorizes that the thermal conductivity can increase due to electron-phonon collisions, where the electrons scatter from the s-band to the d-band. The “disordered” state of the HEA provides an additional mechanism to investigate the thermal conductivity. This works shows, using first-principles calculations of electronic density of states and their derivatives, how to accurately predict the total thermal conductivity including the electron-disorder contribution, and how adding the s-d correction increases the thermal conductivity of the HEAs. This opens an exciting pathway for designing next-generation materials with high thermal conductivity.
Proceedings Inclusion? Planned:
Keywords Modeling and Simulation, High-Entropy Alloys, High-Temperature Materials

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Multi-Fidelity Machine-Learning Workflow for Accelerated Lithium-Ion Cathode Materials Development
Accelerating Quantum Materials Discovery with AI
Agapi-agents: An open-access agentic ai platform for accelerated materials design on atomgpt. org
Assessing, training, and fine-tuning foundation potentials for plasticity-relevant properties of metals
Beyond First Principles: Convergent and Thermodynamically Self-Consistent Atomistic Phase Diagrams from Interatomic Potentials
Capturing Spin-Disorder in Paramagnetic Austenitic Steels: A First-Principles Approach
Characterization of the Atomic-Scale Strengthening Mechanisms in Oxide Dispersion Strengthened Alloys at Elevated Temperatures
Closing the Loop on Superconductor Discovery
Collective motion of screw dislocations in α-Fe and its effect on Peierls stress
Computational discovery of synthesizable and hard multicomponent transition metal carbides
Crystal Structure Exploration Under Extreme Conditions
Defect-Controlled Carbon Transport in Zirconium Carbide under Charge, Temperature, and Pressure Extremes.
Digital material design guidelines for mitigation of alloy embrittlement
Effects of Chemical Fluctuations and Short-Range Order on Dislocation Behavior in Multi-Principal-Element Alloys
Enhancing of the Thermal Conductivity in Refractory-based High Entropy Alloys via Mott s-d Scattering Mechanism
ExactBO: GPU-Accelerated Certified Bayesian Optimization for Continuous and Constrained Materials Design
First-Principles and Graph Learning Framework for Discovery of Li-Ion Transport Pathways in Solid Electrolyte Interphases
First-principles mapping of defect energetics across the 211 MAX phase chemical space
First-Principles Transport in AlGaN/GaN HEMT-like Interfaces
First Principles Data Assisted Investigation of Spin Ordering in Spinel Ni1−xZnxFe2O4 Crystal
From DFT Defect Mechanisms to Transport-Scale Modeling of Hydrogen Isotopes in Li2O
Geometric Design of d- and i-wave Altermagnetism in Graphene Nanohole Superlattices
Humans-(less)-in-the-loop: moving toward autonomous materials design for metal-organic frameworks
Hysteresis in silicon-dominated anodes in lithium-ion batteries.
Integrating Physical Models, Specialized Databases, AI/ML Tools, and Self-Driving Labs to Understand the Synthesis of Inorganic Materials
Intermetallic Composition as a Variable in Alloy Design
Machine Learning-Assisted Design of High-Performance HOR Catalysts for Proton-Conducting Fuel Cells
Machine Learning Screening of Oxide Semiconductors for Energy-Efficient Back-End Transistors
Metadynamics-Accelerated Active Learning of Interatomic Potentials for InP Magic-Sized Cluster Nucleation
PhaseForge: Automated, Uncertainty-Quantified Construction of CALPHAD Thermodynamic Databases via Machine-Learning Interatomic Potentials and Bayesian Parameter Refinement
Physics-Grounded AI for Materials Discovery
Physics-Informed Discovery of Hydride Superconductors
Predicting plateau slope in metal hydrides from first-principles: hydrogen energy and charge distributions in TiMn2-based alloys
Prediction and Screening of Transition Metal Nitrides for Ammonia Synthesis
Quantifying the Contributions of Strengthening Mechanisms in Refractory Complex Concentrated Alloys through Physics-Informed Statistical Learning.
Role of a Ni₁₃ Cocatalyst in Promoting CO₂ Activation and Hydrogenation on CaCu₃Ti₄O₁₂: A First-Principles Study
Size Effects on Chemical Ordering in High-Entropy MXenes: A First-Principles Validated Atomistic Study
Stabilizing Titanium Carbide MXenes in Water with Montmorillonite Heterostructures: Ab Initio Molecular Dynamics Study
Thermodynamic models for the epitaxial growth of oxide solid solutions
Understanding Point Defects in Semiconductors using First Principles Simulations Accelerated by ML Potentials

Questions about ProgramMaster? Contact programming@programmaster.org | TMS Privacy Policy | Accessibility Statement