MACHINE LEARNING WEBINAR SERIES

High-throughput CALPHAD calculations for screening and machine learning of refractory complex concentrated alloys
Speaker: Michael S. Titus
Tuesday February 4, 2025 at 2-3 PM CET/8-9 AM EST 

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Refractory complex, concentrated alloys (also called high entropy alloys) are very high-melting-temperature alloys with significant concentrations of many different alloying elements, and they represent a paradigm shift in alloy design strategies by motivating researchers to explore the entirety of phase diagrams. Using relatively coarse composition grids across 10 potential alloying elements, one can easily generate more than 100,000 unique compositions.

Thanks to highly parallelized processes, the phase equilibria and thermo-physical properties of 100,000 or more compositions can be readily calculated and screened to examine only those exhibiting favorable characteristics, such as single-phase body centered cubic at elevated temperatures. In this webinar, attendees will see a workflow for performing high-throughput CALPHAD calculations using TC-Python implemented in the Thermo-Calc(R) software to screen initially large and high dimensional composition space. 

This webinar will showcase how machine learning and active learning can reduce the number of experiments required to optimize a set of properties from ~5,000 down to just ~20. These efforts simultaneously increase potential useful composition space and dramatically the pace of accelerated alloy design.

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Thermo-Calc Software’s Machine Learning Webinar Series

This webinar is a part of Thermo-Calc Software’s Machine Learning Webinar Series, taking place between November 2024 and February 2025. The series consists of five webinars. The first is by Thermo-Calc Software’s Research and Innovation group and showcases how we are using machine learning in our tools. The other four webinars feature Thermo-Calc users presenting work they have done using our tools together with machine learning. Find more information about the other webinars below.
 

Register for the Other Webinars

CALPHAD by Machine Learning and for Machine Learning │Wednesday November 20, 2024 at 2-3 PM CET/8-9 AM EST

Alloy Design Based on Artificial Intelligence and Machine Learning
│Wednesday December 4, 2024 at 10-11 AM CET/4-5 PM CST
 
AlloyGPT: an agent-based LLM framework for the design of additively manufactured structural alloys in extreme environments │Tuesday January 21, 2025 at 2-3 PM CET/8-9 AM EST 
 
Bayesian Frameworks for Accelerated Alloy Discovery Tuesday January 28, 2025 at 2-3 PM CET/8-9 AM EST
 

About the Speaker

Prof. Michael Titus is an Associate Professor and Technical Director of the Purdue Heat Treating Consortium in the School of Materials Engineering at Purdue University. He earned his B.S. in Engineering Physics from Ohio State (2010) and a Ph.D. in Materials from UC Santa Barbara (2015). He was an Alexander von Humboldt Postdoctoral Fellow at the Max Planck Institute (2015-2016). Prof. Titus has received numerous awards, including the NSF CAREER Award (2018) and ASM Bradley-Stoughton Award (2021). He has published over 40 papers, supervised 10 thesis students, and currently advises 8 Ph.D. students.

michael_titus


Michael S. Titus

Associate Professor of Materials Engineering
Purdue University