I think some of the sibling comments do a decent job of address the how, so I'll loop back to why. Material science research can be expensive due to: material

I think some of the sibling comments do a decent job of address the how, so I'll loop back to why. Material science research can be expensive due to: material costs, specialized equipment, specialized knowledge, dealing with hazardous waste, facility requirements//specialization, competition over the said resources, etc. A computational approach greatly reduces the barrier to entry and expands your reach at the same time. Let's talk phase diagrams as an example. We have some pretty good models for calculating the phase diagrams of bulk materials, but they breakdown as we attempt to describe smaller (I.e. nano) particles. While some experimentation was/is needed to get enough raw data to model additional terms/changes that need to be applied to make the general CALPHAD approach validate at the nanoscale, we can now cheaply test theories about how size/shape/composition of nanoparticles would influence their phase diagrams. This helps cheaply narrow and guide the search space, let's say for nanosolders, that researchers would want to follow up with physical experimentation. This makes a loop, the resultant data is used to make better models which in turn helps point out potentially interesting systems to study.