Phase diagrams-Why they matter and how to predict them

Area of Science:

• Materials Science
• Computational Chemistry
• Physical Chemistry

Background:

• Understanding material thermodynamic stability is crucial for predicting the durability of crystalline polymorphs in pharmaceuticals and designing novel materials.
• Phase behavior analysis informs experimental routes to new materials with desired properties.

Approach:

• This perspective overviews quantifying thermodynamic phase behavior using computer simulations and machine learning (ML) approaches to determine phase diagrams.
• It details the workflow for free-energy computations in condensed phases, including methods like Frenkel-Ladd, thermodynamic integration, and direct-coexistence simulations.
• The synergy between simulations and ML is explored for enhanced phase diagram determination.

Key Points:

• Free-energy calculations provide fundamental insights into material phase behavior.
• Methods discussed are applicable across diverse systems, from materials chemistry to biological phase separation.
• Practical implementation advice for free-energy computations is provided.

Conclusions:

• State-of-the-art free-energy calculations and ML are poised to address challenges in phase diagram determination.
• Future applications include fundamental insights into separation processes using multicomponent solvents.
• Predicting material stability and guiding the discovery of new phases are key outcomes.