Computational Thermodynamics: Recent developments and future potential and prospects

Abstract

1 Motivation Modern technology is often closely connected to the computer-based development of novel, tailor-made alloys with complex composition and competing structural phases. State-of-the-art goals for material scientists are, for example, the adaptive change of crystal structures under mechanical load, the stabilization and size control of precipitates, and the development of alloys with special magnetic, thermal or electrical properties. In most of these cases, knowledge of temperature dependent phase stabilities and the evolution of thermodynamic quantities during phase transformations is required. Therefore, thermodynamic computer simulations, along with extensive materials databases, are becoming increasingly important for scientists and materials producers.The accuracy of thermodynamic predictions is a key issue for computational development of new alloys. The reliability of the thermodynamic databases, therefore, needs to be of the highest possible level. Since all materials are built from the elements in the periodic table, it is evident that a detailed understanding of unaries is required for subsequent accurate modelling of multi-component systems. In order to work systematically towards new and improved materials, it is critical that different research and development activities use the same set of recommended data for these unaries, so that the results can be meaningfully compared. For the desired generalization to multi-component systems, it is additionally important that all unaries are described in a comparable way, i.e., using a similar set of functions.2 CALPHAD method An example of the success of such a strategy is documented by the SGTE (Scientifi c Group Thermodata Europe) unary database, which is part of the CALPHAD (CALculation of PHAse Diagrams) method [1] for anticipating the properties of stable and metastable phase equilibria in multi-component systems. The underlying concept is that of "lattice stabilities'', which has been introduced by Kaufman [2] and which was originally mostly applied to relative stabilities, enthalpy and entropy of transformation. In an empirical fashion mathematical models are employed to describe these properties mainly in terms of polynomials as function of temperature, pressure and for multi-component systems also as function of composition. The parameters of these models for systems with usually less than four components are determined using available experimental data. For the description of a multi-component system the mathematical models of the unaries, binaries and ternaries are combined into databases and the results from Gibbs energy minimisation calculations obtained from such a database are then validated by comparison with experimental data of systems with more than three components.The CALPHAD method is currently the only method that can be used effi ciently with the required accuracy for practical applications in multi-component and multi-phase systems. As a consequenc, the method has become an everyday tool in many industries, including some ...

A long road to a bright future

“…The extended Preface by T. Hickel, U. R. Kattner, and S. G. Fries and five Feature Articles ( [10] and references therein) have been ranking among the most accessed articles of pss (b) ever since January. P. Entel et al propose a promising perspective on materials in the Special Issue "Ferroic Glasses: Magnetic, Polar and Strain Glass", comprising 21 contributions [11].…”

OpenCalphad - a free thermodynamic software

“…It was originally developed for describing the thermodynamics of a system for the calculation of phase equilibria but has been extended to calculations of all kinds of equilibria and other thermochemical properties. Experimental data, as well as data from atomistic methods [6], are used for determining the model parameters. The strength of the CALPHAD method is that it provides a consistent and transparent functional description of the properties of the unary, binary, and ternary subsystems that can be combined for the calculation of a multicomponent system.…”

Assessing Phase Diagram Accuracy

“…Although mean square error-type quantities are often well-suited for this purpose when the predicted quantities are simple functions or vectors, quantifying errors in a more general graph, such as phase diagram, in a single figure-of-merit represents a considerably more complex problem. Given this, it is perhaps not surprising that the area of research focusing on the construction of thermodynamic models [often referred to as CALculation of PHAse Diagram (CALPHAD)] [5,[10][11][12]15] is currently lacking a theoretically justified and widely adopted figure-of-merit to quantify the discrepancies between two possible phase diagrams obtained via different routes. This paper intends to fill this gap by building upon earlier proposals.…”