Abstract

Results from DFT calculations are in many cases equivalent to experimental data. They describe a set of properties of a phase at a well-defined composition and temperature, T, most often at 0 K. In order to be practically useful in materials design, such data must be fitted to a thermodynamic model for the phase to allow interpolations and extrapolations. The intention of this paper is to give a summary of the state of the art by using the Calphad technique to model thermodynamic properties and calculate phase diagrams, including some models that should be avoided. Calphad models can decribe long range ordering (LRO) using sublattices and there are model parameters that can approximate short range ordering (SRO) within the experimental uncertainty. In addition to the DFT data, there is a need for experimental data, in particular, for the phase diagram, to determine the model parameters. Very small differences in Gibbs energy of the phases, far smaller than the uncertainties in the DFT calculations, determine the set of stable phases at varying composition and T. Thus, adjustment of the DFT results is often needed in order to obtain the correct set of stable phases.

Highlights

  • In materials physics the scientists performing density functional theory (DFT) calculations have a preference for the cluster variation method (CVM) or Monte Carlo (MC) methods to calculate the equilibria in their systems because these can describe both long range ordering (LRO) and short range ordering (SRO) in the crystalline phases

  • In an assessment of the Au–Cu system, Sundman et al [3] derived a first order approximation of the SRO contribution to compound energy formalism (CEF) by comparing the configurational entropy in a quasichemical model for BCC to that of a recipocal model. They could show that a reciprocal parameter, with a value related to the bond energy, gives a topologically correct phase diagram with separate maxima for ordered L12 and L10 for a four-sublattice CEF model of Au–Cu

  • Results from DFT calculations fill an important gap in the data needed to determine thermodynamic properties for phase diagram calculations when experiments are very complicated or impossible

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Summary

Introduction

In materials physics the scientists performing density functional theory (DFT) calculations have a preference for the cluster variation method (CVM) or Monte Carlo (MC) methods to calculate the equilibria in their systems because these can describe both long range ordering (LRO) and short range ordering (SRO) in the crystalline phases. The Calphad models based on the compound energy formalism (CEF) [2], as described in this paper, are computationally highly efficient and make it possible to calculate multi-component equilibria and phase diagrams for systems with many components and phases. Such phases can feature four or more sublattices to describe complex states of LRO and SRO.

The Pure Element Data
The New Unary Database
Terminology
Modeling Long and Short Range Ordering
Using the Reciprocal Model in CEF to Model LRO and SRO
The Endmember Concept and Metastability
The Reciprocal Energy and Miscibility Gap
The Reciprocal Model for Order–Disorder Transitions
An Approximation of the SRO Contribution
The Disordered Fraction Set
CEF Calculations for Prototype FCC Ordering
Fe–Ni as an Example for Modeling with DFT
Effective Cluster Interaction
CVM Calculation
CEM and CVM Calculations for Fe–Ni
CEF Calculation for Fe–Ni
The Experimentally Assessed Fe–Ni System
Examples Using DFT Data for CEF Models with LRO and SRO
Intermetallic Phases
The σ Phase and Other TCP Phases
The Effective Bond Energy Formalism
CEF Models with Ionic Constituents
CEF Models for Defects
Phases with Order–Disorder Transitions
BCC Ordring Using Two Sublattices
Never Model FCC Ordering with Two Sublattices
The Four-Sublattice Model for Order–Disorder
Modeling SRO in Liquids
Assessments Using CEF Models Including ab initio Data
The Number of Sublattices to Use
Reducing the Number of Endmember Parameters
Mechanically Unstable Endmembers
Estimating Endmember parameters
Conclusions
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