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Meeting 2023 TMS Annual Meeting & Exhibition
Symposium Accelerated Discovery and Insertion of Next Generation Structural Materials
Sponsorship TMS Materials Processing and Manufacturing Division
TMS: Phase Transformations Committee
TMS: Integrated Computational Materials Engineering Committee
Organizer(s) Soumya Nag, Oak Ridge National Laboratory
Andrew Bobel, General Motors Corporation
Bharat Gwalani, North Carolina State Universtiy
Jonah Klemm-Toole, Colorado School of Mines
Antonio J. Ramirez, Ohio State University
Matthew A. Steiner, University of Cincinnati
Scope Structural stability of aerospace and energy related materials, manufactured by conventional and additive routes, is of great importance to avoid catastrophic failures during operation. Understanding their thermo-mechanical response under extreme pressure, temperature or corrosive conditions would immensely aid in designing alloys, and thereby increasing their lifetimes. This symposium delves into investigations, focused on using high throughput tools for accelerated materials discovery and root cause analyses of fielded and new make parts.

The topics of interest to this symposium include, but are not limited to, the following:
•ICME tools coupled with multi-scale experimentation to correlate processing history to microstructural hierarchy and ensuing property response
•ML-based multi objective optimization models targeted towards more reliable predictive capabilities with realistic (usually small) experimental data
•High throughput experimental approaches for accelerated material-microstructure-property optimizations to facilitate ML.

The focus is on structural high temperature and light-weight materials such as refractory alloys, high entropy alloys, Ni- Co- based alloys, high strength titanium alloys, maraging steels and ODS alloys.

Abstracts Due 07/17/2022
Proceedings Plan Planned:
PRESENTATIONS APPROVED FOR THIS SYMPOSIUM INCLUDE

A design space for tunable ceramic-polymer composites
A diffusion couple approach to β-Ti alloy development: evaluating the oxidation performance of Ti-Fe-X+ alloys
A high-throughput setup for materials exposure to simultaneous irradiation-corrosion conditions
Accelerated Discovery of Novel Titanium Alloys using High-throughput Manufacturing, Characterization and Testing
Accelerating Multimodal Data Collection: A Workflow for Metallic Films
AI and Machine learning tools for development and analysis of image driven 2D materials
Combinatorial mechanical microscopy via correlated nanoindentation and EDX mapping
Computational design of an ultra-strong high-entropy alloy
Computational Design of High Entropy Alloy Hardmetals
Data Efficient Bayesian ICME Workflow for the Design of Targeted Mechanical Properties of Structural Materials
Design of a Compact Morphology Cobalt-Based Superalloy for Additive Manufacturing
Development of Vickers hardness prediction models via microstructural analysis and machine learning
Efficient conductivity and hardness optimization in Cu-Ag-Ni alloys using Bayesian active learning
High-Throughput Electric-Field-Assisted Sintering and Characterization Techniques for Materials Discovery
High-Throughput Prediction of Fracture and Brittle to Ductile Transition in Tungsten Using Variable Temperature Nanoindentation
High-Throughput Synthesis and Mechanical Characterization of Sputtered Metallic Alloys
How should you select an algorithm for a materials discovery campaign with multiple objectives, complex and high-dimensional structure-processing-property relationships, and a small adaptive design budget?
Machine learning-assisted discovery of novel High Temperature Ni-rich NiTiHfZr multi-component shape memory alloys
Rapid characterisation of active slip systems in titanium ordered-bcc compounds using an algorithm for automated indentation slip trace analysis.
Using Machine Intuitive Learning to Predict Advanced Steel Properties


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