Affiliation:
1. Department of Mechanical Engineering École de Technologie Supérieure (ÉTS) Montreal Canada
2. Department of Materials Science and Engineering University of Toronto Toronto Canada
3. Department of Mining and Materials Engineering McGill University Montreal Canada
Abstract
AbstractKnowledge of thermodynamics and phase equilibria of oxidic materials is crucial for advancement in the field of ceramics and glass. With the development of computational thermodynamics, predicting phase diagrams and chemical reactions of multicomponent systems has become possible. However, there are still plenty of oxides, the thermodynamic properties of which have not been identified due to the challenges in conducting experiments. Therefore, a key to the advancement in thermodynamic modeling would be to develop a universal model that can be used to estimate the thermodynamic properties of oxides with reliable extrapolation capacity. Atomistic (or molecular) scale models are still insufficient in predicting the thermodynamic properties of oxides at any scale. Alternatively, among group contribution–based methods, the polyhedron model has presented its potential in the estimation of the thermodynamic properties of ionic crystals. However, this model still demands improvements that increase the model's accuracy and extrapolation capacity. In this paper, the background and the state‐of‐the‐art of polyhedron model will be presented together with its strengths and shortcomings. Subsequently, it will be briefly discussed how the field of artificial intelligence could be exploited to devise the next generation of the polyhedron model, the modified polyhedron model.
Subject
Materials Chemistry,Ceramics and Composites
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献