As a species, we are particularly proud of our capacity for creative thought. Our ability to invent tools and imagine abstract concepts distinguishes us from other animals and from modern day computers (for example, the Turing Test is essentially a clever form of a creativity test).
However, in many cases, humans can now program computers to solve problems better than they can. In 1987, chess grandmaster Garry Kasparov’s proclaimed that “No computer can ever beat me”. The statement was perfectly reasonable at the time, but it would be proven wrong within a decade. Similar turning points in history won’t be limited to games. For a 2006 NASA mission that required an unconventional antenna design, the best-performing, somewhat alien-looking solution was not imagined by a human antenna design expert (of which NASA surely had plenty) but was instead evolved within a computer program. When mission parameters changed, a new design was re-evolved for the new specifications much more rapidly than could be achieved by NASA staff.
It is difficult to say whether current achievements by computers truly constitute “creativity” or “imagination”. However, the philosophical ambiguity has not stopped materials researchers from joining in on the action. As one example, Professor Artem Oganov’s lab at Stony Brook University has used computer programs to evolve new materials that have been observed at high pressures and might play a role in planet formation. These materials can be quite unexpected, such as a new high-pressure form of sodium that is transparent and insulating rather than silver and metallic. Thus, while we may not know whether to label the computer’s process as a “creative” one, the end result can certainly possess the hallmarks of purposeful design.
Indeed, if there is any doubt that computer algorithms are capable of producing creative solutions, one needs only to visit the Boxcar2D site. This website uses evolutionary algorithms to design a two-dimensional car from scratch; the process unfolds before your eyes, in real time.
It is instructive to observe the Boxcar2D algorithm “thinking”. Towards the beginning of the simulation, most designs are underwhelming, but a few work better than you’d intuit. For example, my simulation included a one-wheeler that employed an awkward chassis protrusion as a brake that modulated speed during downhill sections. It was a subtle strategy that outperformed a more classic motorbike design that wiped out on a mild slope.
Eventually, the one-wheelers would prove too cautious, and the algorithm began designing faster two-wheelers that better matched the size between the wheels and the proportions of the frame to prevent flipping. Finally, the algorithm designed a car that was symmetric to being turned upside down, eliminating the problem altogether. All this happened in the course of minutes in a somewhat hypnotic visual progression from random to ordered.
However, despite the successes in computational optimization, several obstacles still exist that prevent future materials from being designed by computers. The biggest problem is that computers cannot yet predict all the important properties of a material accurately or automatically. In many cases, computational theory can only predict a few heuristic indicators of less than half the important technological properties of a material. Thus, materials that look promising to the computer are incomplete models that require further evaluation and perhaps re-ranking by human experts. Indeed, the most successful automatic algorithms have been those used for crystal structure prediction (such as the research of Professor Oganov), for which simple computer calculations are very good at ranking different compounds without adult supervision.
There are other problems; for example, genetic algorithms typically require many more calculations to find a good solution compared with human-generated guesses. However, this “problem” may also be an advantage. The computer’s willingness to produce several rounds of very poor-performing, uninhibited designs frees it from the bias and conservatism that can be displayed by human researchers, thereby revealing better solutions in the long run. Still, helping the computer become a smarter and more informed guesser would certainly improve the prospects for designing materials in computers.
Today, almost all materials are still devised by humans within a feedback loop of hypothesis and experiment. The next step might be to mix human and machine – that is, to use human intuition to suggest compounds that are further refined by a computer. Yet, perhaps one day, materials design may become more like chess or antenna design. Like parents of a gifted child, it might become more logical for materials scientists to train their computers to be more imaginative than them.Footnotes:
 A nice illustrated history of artificial intelligence in games was presented by XKCD.
 More about NASA’s evolved antenna design here.
 Attempt to evolve cars from randomness at the Boxcar2D site.
 For example, my colleagues (with minor help from myself) devised data-driven algorithms for materials prediction that more closely mimic a first step undertaken by many researchers (links here and here). These algorithms are more efficient at finding new materials but are much less “creative” than the evolutionary algorithms employed by Professor Oganov and others.