ProgramMaster Logo
Conference Tools for 2023 TMS Annual Meeting & Exhibition
Register as a New User
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools
About this Abstract
Meeting 2023 TMS Annual Meeting & Exhibition
Symposium Accelerated Discovery and Insertion of Next Generation Structural Materials
Presentation Title Efficient Conductivity and Hardness Optimization in Cu-Ag-Ni Alloys using Bayesian Active Learning
Author(s) Terrance Life, Shankarachary Ragi, Bharat Jasthi, Ananth Kandadai
On-Site Speaker (Planned) Terrance Life
Abstract Scope The traditional method to develop and improve materials with desired properties is through trial and error, a time-consuming process requiring domain experts to propose, fabricate, and test numerous potential materials. Our proposed method to accelerate this is to utilize combinatorial deposition and Bayesian active learning to efficiently solve a multi-objective optimization problem for thin-film materials. We demonstrate our method through the fabrication of a copper-silver-nickel alloy in which the percentages of each element vary throughout the sample. This allows the sample to represent a range of potential alloys which our method explores to optimize the conductivity and surface hardness, guided by a Bayesian framework based on Gaussian Process models. The predictions from these models permit the system to selectively test only the alloys most likely to yield the optimal properties. This concludes with an analysis of the global Pareto front, which guides the synthesis of future test samples.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, Machine Learning, Characterization


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

Questions about ProgramMaster? Contact