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Meeting 2023 TMS Annual Meeting & Exhibition
Symposium Computational Discovery and Design of Materials
Presentation Title Design and Development of High Strength High Conductivity Alloys using ICMDŽ Approach
Author(s) Qiaofu Zhang, Tom Kozmel, Peter Jacobson, Jiadong Gong, Greg Olson
On-Site Speaker (Planned) Qiaofu Zhang
Abstract Scope Electrification is the dominant strategy for decarbonization of industry in order to meet the greenhouse gas reduction goal. This electrification trend requires novel materials developments of materials/alloys with high conductivity to reduce energy losses, and high strength to provide structural support and adoption to various conditions. Using QuesTek’s Materials By DesignŽ approach, several HSHC alloys have been designed and developed for different applications. At the computational design stage, ICME models were developed to connect the correlation link of process-microstructure-properties, and alloys were designed using the developed models and QuesTek’s in-house Integrated Computational Materials Design (ICMDŽ) platform. Experimental prototyping were performed to achieve the desired microstructure and target properties to validate the design. Three types of alloys, Cu-alloys, Al-alloys, and carbon-nanotube (CNT) additive materials will be discussed in this presentation.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, Modeling and Simulation, ICME

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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
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Machine-learning-boosted Searching and Optimization of Emergent Quantum Materials
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Modeling of Local Lattice Distortion Effects on Vacancy Migrations in Multicomponent FCC Alloys
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