Webinar
MACHINE LEARNING SERIES
Bayesian Frameworks for Accelerated Alloy Discovery
This webinar includes a discussion of novel approaches to alloy discovery through Bayesian approaches. The framework that is 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 presents a few examples where these methods have been used to optimize alloys under multiple objectives and constraints.
This webinar is a part of Thermo-Calc Software’s Machine Learning Webinar Series, taking place between November 2024 and February 2025. Find more information about the other webinars below.
High-throughput CALPHAD calculations for screening and machine learning of refractory complex concentrated alloys
[ON-DEMAND] CALPHAD by Machine Learning and for Machine Learning
[ON-DEMAND] Alloy Design Based on Artificial Intelligence and Machine Learning