AIChE Journal Highlight: Hybrid AI-Physics Models Accelerate Crystal Property Prediction for Fast Drug Development

Crystallization has long stood at the heart of the chemical and pharmaceutical industries, serving as a primary method for purification, separation, and formulation. From the earliest large-scale crystallizers in the fine chemicals sector to today’s precision pharmaceutical manufacturing, the ability to control crystallization directly impacts product stability, drug bioavailability, and overall process efficiency. In fact, it is estimated that nearly 80% of active pharmaceutical ingredients undergo at least one crystallization step during manufacturing. The properties of the resulting crystals, most importantly the crystal size distribution (CSD), govern not only the manufacturability of a compound but also its therapeutic performance.

For decades, researchers have sought to predict and control CSD through first-principles modeling. Population balance equations (PBEs), mass balances, and energy balances offer a rigorous framework to capture nucleation and growth phenomena. However, these models rely on parameters that span both thermodynamics (such as solubility and solvation free energy) and kinetics (such as crystal growth rates). Obtaining these...