What are the best ways to model material behavior under extreme conditions?

1 Computational thermodynamics

Computational thermodynamics is the application of thermodynamic principles and models to calculate the equilibrium state and properties of materials. For example, you can use computational thermodynamics to determine the phase diagrams, phase transformations, chemical reactions, and thermophysical properties of materials under different conditions. One of the most common tools for computational thermodynamics is the CALculation of PHAse Diagrams (CALPHAD) method, which uses experimental and theoretical data to fit and optimize thermodynamic models for each phase of a material system.

2 Computational kinetics

Computational kinetics is the application of kinetic principles and models to calculate the non-equilibrium state and properties of materials. For example, you can use computational kinetics to determine the diffusion, nucleation, growth, coarsening, and precipitation of phases in materials under different conditions. One of the most common tools for computational kinetics is the Phase Field method, which uses partial differential equations to describe the evolution of phase boundaries and microstructures in materials.

3 Coupling thermodynamics and kinetics

Sometimes, it is not enough to model the thermodynamics or kinetics of materials separately, as they can affect each other in complex ways. For example, the phase transformations and chemical reactions of materials can depend on the diffusion and nucleation rates, which in turn can depend on the thermodynamic driving forces and barriers. Therefore, some materials scientists use coupling methods to combine the thermodynamics and kinetics of materials in a consistent and coherent way. One of the most common coupling methods is the Thermodynamic-Kinetic-Microstructure (TKM) approach, which integrates the CALPHAD, Phase Field, and Finite Element methods to simulate the coupled thermodynamics, kinetics, and mechanics of materials.

4 Validating and verifying models

Of course, computational models are not perfect and need to be validated and verified against experimental data and physical laws. Validation is the process of checking whether the model can accurately reproduce the observed behavior and properties of materials under different conditions. Verification is the process of checking whether the model is implemented correctly and consistently with the assumptions and equations. Some of the methods for validating and verifying models are sensitivity analysis, uncertainty quantification, error estimation, and benchmarking.

5 Optimizing and designing materials

One of the main goals of modeling material behavior under extreme conditions is to optimize and design materials for specific applications and performance criteria. For example, you can use modeling to find the optimal composition, microstructure, processing, and loading conditions for a material that can withstand high temperature, pressure, or stress. Some of the methods for optimizing and designing materials are multi-objective optimization, inverse design, machine learning, and artificial intelligence.

6 Learning and improving models

Finally, modeling material behavior under extreme conditions is not a static or final process, but a dynamic and iterative one. As new data, models, methods, and tools emerge, you can use them to learn and improve your existing models. For example, you can use experimental data to update and refine your thermodynamic and kinetic models, or you can use machine learning and artificial intelligence to discover new patterns and relationships in your data and models. Some of the methods for learning and improving models are data mining, data assimilation, model selection, and model updating.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?