MACHINE LEARNING WEBINAR SERIES

Bayesian Frameworks for Accelerated Alloy Discovery
Speaker: Raymundo Arróyave
Tuesday January 28, 2025 at 2-3 PM CET/8-9 AM EST 

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This webinar will include a discussion of novel approaches to alloy discovery through Bayesian approaches. The framework that will be presented combines experiments and simulations to arrive at optimal discovery sequences capable of identifying optimal materials with minimal resource utilization. A key element in this approach to iterative materials optimization is the use of CALPHAD-based simulation workflows (executed through Thermo-Calc’s Python API) to calculate phase stability and performance-relevant properties. By using these models, efforts can greatly reduce the space to explore such that the Bayesian exploration is carried out in a much more manageable design space. In the webinar, the speaker will present a few examples where these methods have been used to optimize alloys under multiple objectives and constraints.

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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 team 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

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 
 
High-throughput CALPHAD calculations for screening and machine learning of refractory complex concentrated alloys │ Tuesday February 4, 2025 at 2-3 PM CET/8-9 AM EST
 
CALPHAD by Machine Learning and for Machine Learning │ON-DEMAND
 
Alloy Design Based on Artificial Intelligence and Machine Learning ON-DEMAND
 

About the Speaker

Dr. Arróyave earned BS degrees in Mechanical and Electrical Engineering from Instituto Tecnológico y de Estudios Superiores de Monterrey (Mexico) in 1996, and an MS (2000) and PhD (2004) in Materials Science from MIT. Following a postdoc at Penn State, he joined Texas A&M University in 2006, where he is now a Professor in Materials Science and Engineering, with courtesy appointments in Mechanical Engineering and Industrial and Systems Engineering.

His expertise lies in computational materials science, focusing on thermodynamics, kinetics, and multi-scale simulations of metallic alloys and ceramics. His recent work emphasizes simulation- and data-driven materials discovery and design, particularly for Additive Manufacturing. He has published over 270 peer-reviewed papers, 20 conference proceedings, nearly 200 conference papers, and given 120+ invited talks globally.

Dr. Arróyave's honors include the NSF CAREER Award (2010), TMS Brimacombe Medal (2019), ASM Fellow (2020), and Acta Materialia Silver Medal (2023). He is a Texas A&M Presidential Impact Fellow and EDGES Fellow and serves as Editor-in-Chief of Materials Letters and Associate Editor for other journals. Active in ASM and TMS, he has chaired numerous symposia, committees, and international conferences.

Arroyave_Photo
Raymundo Arróyave
Professor
Department of Materials Science and Engineering
Texas A&M University