ProgramMaster Logo
Conference Tools for 2022 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 2022 TMS Annual Meeting & Exhibition
Symposium Frontiers of Materials Award Symposium Session: Data-Driven, Machine-learning Augmented Design and Novel Characterization for Nano-architectured Materials
Presentation Title Accelerated Discovery of Multi-phase Refractory Alloys through Machine Learning Surrogate Models of CALPHAD
Author(s) Samuel Price, Ian McCue, Jonah Erlebacher
On-Site Speaker (Planned) Samuel Price
Abstract Scope There is a great need for alloys that can operate at higher temperatures than Ni-base superalloys. Refractory alloys (e.g., consisting of Nb, Ta, Mo, and W) are promising alternatives, but their single-phase microstructures impair high-temperature mechanical performance. Thus, there is an impetus to design multi-phase refractory alloys with coherent precipitates that are stable to high temperatures. The vast compositional design space for such alloys necessitates the use of computational tools. One such tool, CAlculation of PHAse Diagrams (CALPHAD), can be used to screen alloys by predicting their equilibrium phases; however, CALPHAD is too slow to screen these large datasets. Here, we have solved this issue by developing accurate surrogate models of CALPHAD using artificial neural networks. Our new approach is 20x faster than the traditional “brute force” method, making it possible to search larger alloy systems in a matter of hours and days rather than weeks and months.
Proceedings Inclusion? Undecided
Keywords High-Temperature Materials, Machine Learning, Phase Transformations


Accelerated Discovery of Multi-phase Refractory Alloys through Machine Learning Surrogate Models of CALPHAD
Autonomous X-ray Scattering for the Study of Non-equilibrium Self-assembly
Designing Nano-architectured Materials with a Machine-learning Augmented Framework
Discovery of Nanocomposite Phase Change Memory Materials via Closed-loop Autonomous Combinatorial Experimentation
Intelligent Design of Additively Manufactured Architected Materials
Machine Learning Based Hierarchical Multi-scale Modeling of Mechanical Deformation for Metal-matrix-nano-composites
Volumetric Nanoscale Imaging of DNA-assembled Nanoparticle Superlattices
“Big Data” Characterization of Material Properties and High Temperature Kinetics

Questions about ProgramMaster? Contact