Before long, we dropped “Genome” from the project name to distinguish it from an initiative that the White House Office of Science and Technology Policy was launching. And to be fair, the properties of chemical compounds are not really “genes”—they are not hereditary bits of information that provide a unique sequence of data. Still, a direct relation exists between the function or property of a material and its fundamental descriptors. Just as blue eyes can be correlated to a certain gene, the electronic conductivity of a material, for example, can be traced back to the properties and arrangements of the elements it is composed of.
These kinds of correlations are the basis of materials science. Here is a simple example: we know we can “tune” the color of minerals by introducing targeted defects into their crystal structure. Consider the ruby. Its red hue comes from an accidental 1 percent substitution of a chromium ion (Cr3+) for aluminum in the common mineral corundum (Al2O3). When the Cr3+ is forced into this environment, its electronic states become altered, which changes the way the material absorbs and emits light. Once we know the origin—the fundamental descriptor—of a property (in this case, the redness of a ruby), we can target it with synthetic methods. By tweaking those chemical defects, we can design new synthetic rubies with perfectly tuned colors.
The equations of quantum mechanics can tell us how to do that tweaking—what elements to use and how to arrange them. Yet the equations are so complex that they can really only be solved by computer. Say you want to screen a group of a few hundred compounds to see which ones have the properties you need. It takes an incredible amount of computing power to crunch those equations. Until recently, it simply was not possible, which is why so much of materials science has historically proceeded by trial and error. Now that we have the computing power, however, we can finally take advantage of the full predictive potential of quantum mechanics.
Suppose we are researching thermoelectric materials, which generate an electric current if they experience a large temperature gradient. (The reverse is also true: a thermoelectric material can sustain a temperature difference if you run a current through it; think instant cooling.) Society wastes an enormous amount of heat through combustion, industrial processing and refrigeration. If we had efficient, cheap and stable thermoelectric materials, we could capture this heat and reuse it as electricity. Thermoelectric devices could transform industrial waste heat into electricity to power factories. Heat from car exhaust pipes could power the electronics in the cockpit. Thermoelectrics could also provide on-demand solid-state cooling: little devices that we could weave into our clothing that, with a flip of a switch, would cool us down, no fans or compressors required.
One of the best thermoelectrics we know of today is lead telluride, which is far too toxic and expensive to use commercially. Suppose you are a researcher looking for a better thermoelectric material. Without high-throughput computing, this is how it would go: You would start by looking for known compounds that, like lead telluride, have a high Seebeck coefficient (a measure of the amount of electricity you get out for the temperature difference that goes in) but that, unlike lead telluride, are not made of rare, toxic or expensive elements. You would pore over tables and compare numbers. If you were lucky, you would come up with some candidate chemistries that, in theory, would seem like they could work. Then you would make those compounds in a lab. Physically synthesizing materials is an expensive, time-consuming and difficult job. Generally, you have no idea going in whether the new material will even be stable. If it is, you can measure its properties only after you have synthesized the compound and then repeated the process until you have a fairly pure sample. This can take months for each compound.