Deciphering the complexities of crystalline state(s) with molecular simulations

Area of Science:

• Solid state chemistry and materials science.
• Computational chemistry and condensed matter physics.

Background:

• Predicting crystallization outcomes is a major challenge in solid state chemistry.
• The complex interplay between thermodynamics and kinetics creates intricate crystal energy landscapes with multiple polymorphs and metastable states.
• Understanding these factors is crucial for controlling solid form outcomes.

Purpose of the Study:

• To demonstrate how recent computational advancements, augmented by Machine Learning (ML), enhance the prediction of crystal structure.
• To showcase the application of these methods in simulating crystal nucleation processes.
• To provide a high-resolution view of crystallization mechanisms.

Main Methods:

• Utilizing molecular simulations to compute free energies (thermodynamics) and kinetic barriers.
• Employing advanced computational techniques, enhanced by Machine Learning algorithms.
• Visualizing crystallization mechanisms at high resolution.

Main Results:

• Significant progress has been made in predicting crystal structure using computational methods.
• The integration of Machine Learning has further advanced simulation capabilities.
• Accurate simulation of crystal nucleation is now more achievable.

Conclusions:

• Molecular simulations offer a powerful framework for understanding the thermodynamics and kinetics of crystallization.
• Recent computational and ML advancements have overcome previous limitations in predicting crystal structure and nucleation.
• These improved methods provide deeper insights into complex crystallization processes.