Imagine being tasked with baking a soufflé, except the only instruction provided is an ingredient list without any measurements or temperatures.
It would likely take an enormous amount of time, effort and ingredients to bake the perfect soufflé. It would require trial and error—tweaking ingredient measurements, altering the temperature and baking duration—but what if you had a model that could predict the final product before anything ever went into the mixing bowl? It would not only save weeks’ worth of time and resources but could also provide useful details like why and how the soufflé rose and collapsed when it did or why the texture didn’t turn out how you expected.
Researchers at the Beckman Institute for Advanced Science and Technology aren’t quite baking soufflés. Instead, they developed a computational model that digs into the chemical “recipe” of polymer manufacturing to provide predictive control over how materials self-organize to give rise to new textures and properties.