|About this Abstract
||2018 TMS Annual Meeting & Exhibition
||Computational Design and Simulation of Materials (CDSM 2018): Computational Design of Materials
||Uncertainty of Thermodynamic Data for Materials Design
||Marius Stan, Noah Paulson
|On-Site Speaker (Planned)
Thermodynamic properties and phase equilibria of multi-component materials are important design criteria for basic science and engineering applications. Unfortunately, many journal articles report thermodynamic data – especially phase equilibrium diagrams – without uncertainty/confidence intervals. The main reason for the lack of uncertainty quantification is the complexity of the physics and chemistry of the multi-component materials. In addition, there are mathematical and computational challenges associated with quantifying uncertainty in a multi-dimensional parametric space. In this talk we discuss uncertainty evaluation of thermodynamic data with a focus on high-temperature phase equilibria and associated thermodynamic properties. The scientific approach is based on a Bayesian method that simultaneously accounts for different types of data and its provenance and delivers uncertainty intervals. Data analysis is enhanced by machine learning methods. The presentation ends with a discussion of the changing role of computation in materials design.
||Planned: Supplemental Proceedings volume