Digital Design of Crystals: Predicting Driving Forces for Crystallization Using Atomistic Simulations
One of the most awe-inspiring class of materials are crystals. These highly ordered groups of atoms/molecules propel our lives through myriad products we humans rely upon. Right from the salt and sugar we consume to elevate the taste of our foods, the silicon chips that form the brains of every computing device we use, to the life-saving drugs that have prevented millions if not billions of deaths, all belong to the humble class of materials — the crystal. Therefore, the engineering of this material is crucial to — improve the manufacturing of products that touch our lives daily — ultimately improving the quality of every human life!
The two physical attributes of a crystalline material that have a major impact on its processability and performance are its shape and size. For e.g., in a crystalline catalyst one wants to engineer the shape of the crystal to maximize the area of its reactive surfaces. In pharmaceutical applications, the crystal size distribution may determine the rate of plasma uptake of a drug when the process is dissolution rate limited. Therefore, engineering the shape and size of the crystals is of immense consequence.
In silico tools that can predict the shape and size of a crystal based on inputs such as crystal structure, temperature, supersaturation, etc. are vital to efficiently navigate the process design space. Such tools help achieve the efficiency gains by being a guiding light to experimentalists, thus enabling cheaper and more effective screening. This dissertation lays out the digital design framework to make these predictions starting from a molecule. It focuses on the development of a computational toolkit to predict driving forces for crystallization — a key prediction to enable size predictions — of complex molecules harnessing atomistic simulations.