Predictive thermodynamic models, developed by the Calphad method, are essential for designing new materials by anticipating their behavior without resorting to costly and time-consuming experiments. These models allow for the extrapolation of the properties of complex materials, predicting their behavior in extreme environments, and linking energy properties to in-service performance. However, current methods for developing these models are complex, and uncertainties are not quantified in existing software. Scientists still rely on their expertise to adjust and validate these models, which is time-consuming and poorly suited to the era of automation.
To address this, it is proposed to develop a reliable, autonomous, and fast digital tool capable of optimizing thermodynamic models based solely on experimental data provided by users. The goal is to provide simple, reliable, validated, and modular models, enabling users to make strategic decisions with confidence, such as evaluating new process conditions or optimizing manufacturing without risking uncertain extrapolations. This project aims to bridge the gap between specific experimental data and modern nonlinear programming methods, using advanced optimization approaches.