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Meeting 2023 TMS Annual Meeting & Exhibition
Symposium Accelerated Discovery and Insertion of Next Generation Structural Materials
Presentation Title Accelerating Multimodal Data Collection: A Workflow for Metallic Films
Author(s) Kimberly Bassett, Brad Boyce
On-Site Speaker (Planned) Kimberly Bassett
Abstract Scope Accelerated materials discovery through statistical or machine learning methods necessitates the generation of large data sets. While automated workflows for materials characterization proliferate in industry, specific tools for smaller laboratories are scarce in the literature. Here, we detail a general workflow for accelerating the multimodal characterization of metallic electrodeposited films in pursuit of improving their tribological properties. Methods such as sample tracking, maximization of available instrument features, custom sample fixturing, and strategies to overcome instrument limitations facilitate process acceleration to achieve a high throughput workflow, minimize human bias during characterization, and expedite repetitive tasks. The workflow efficacy is evaluated by key factors such as data repeatability and human capital saved. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA-0003525.
Proceedings Inclusion? Planned:
Keywords Characterization, Copper / Nickel / Cobalt, Other

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

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
Design of a Compact Morphology Cobalt-based Superalloy for Additive Manufacturing
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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