About this Abstract |
Meeting |
MS&T22: Materials Science & Technology
|
Symposium
|
Uncertainty Quantification in Data-Driven Materials and Process Design
|
Presentation Title |
Efficient Phase Diagram Determination via Sequential Learning |
Author(s) |
Theresa Davey, Brandon J Bocklund, Zi-Kui Liu, Ying Chen |
On-Site Speaker (Planned) |
Theresa Davey |
Abstract Scope |
Phase diagrams are a fundamental tool for materials design, but thorough experimental exploration of the composition and temperature space is challenging, expensive, and time consuming. Despite recent strides in the development of theoretical methods for phase diagram calculations, experimental investigation remains essential to validate the predictions for a high accuracy description. The Gibbs energy of liquid and disordered phases is challenging to obtain directly from first-principles calculations, so other methods are required to fully elucidate the correct phase diagram topology. Considering the quantified uncertainty of the phase diagram, a sequential learning approach is developed to systematically add data in regions of highest uncertainty. Fictitious experimental data is generated and used in automatically optimising a principles-only thermodynamic database. The convergence of the system is examined as various “experimental” data sets are used, demonstrating the selection of an efficient experimental pathway to a high accuracy description. |