Webinar - CALPHAD by Machine Learning and for Machine Learning

MACHINE LEARNING WEBINAR SERIES

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

Artificial intelligence and machine learning (AI/ML) has emerged as a transformative force in materials science, presenting both challenges and opportunities. At Thermo-Calc Software AB, we are striving toward a full integration of AI/ML into our CALPHAD (CALculation of PHAse Diagrams) framework, enabling new approaches for rapidly developing high-quality thermodynamic and kinetic databases as well as accurate and fast-acting materials property models that are critical to materials design.

This webinar, CALPHAD by Machine Learning and for Machine Learning, will explore how CALPHAD data can empower machine learning applications, and how, in turn, ML can enhance CALPHAD methodologies.

In this webinar, we will cover:

Harnessing AI/ML for CALPHAD Database Development: Insights into our internal projects and the remarkable results achieved.
Empowering Machine Learning with CALPHAD Data: How Thermo-Calc calculation results can be utilized to enhance AI/ML models for accelerated materials design.
Success Stories and Pitfalls: Case studies showcasing the effective and problematic integration of CALPHAD calculations in addressing materials science challenges through Machine Learning.
Guidance on Software and Data Usage: Clarifications on the use of Thermo-Calc Software's tools and data in training ML models, in accordance with our End User License Agreement.

Join us to discover how the fusion of CALPHAD and machine learning can turn emerging technology into a powerful toolset for the future of materials science, transforming the way we approach database development, predictive materials property modeling, and beyond.

Register for the Webinar

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

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

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

About the Speaker

Prof. Qing Chen is the Chief Scientific Officer and Director of Research and Innovation at Thermo-Calc Software AB, where he oversees the company’s scientific endeavors and drives research and innovation initiatives. He also serves as an Adjunct Professor in Applied Thermodynamic Modeling at KTH Royal Institute of Technology in Stockholm, Sweden. With over 30 years of experience, Prof. Chen specialized in CALPHAD-based modeling of phase diagrams, phase transformations, microstructural evolution, and thermophysical properties. His recent research focuses on integrating CALPHAD, DFT, and AI/ML methodologies for advanced materials design. He has authored about 100 scientific papers.

Prof. Qing Chen
Chief Scientific Officer and Director of Research and Innovation
Thermo-Calc Software AB