How Computing Is Unleashing a Materials Revolution

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How Computing Is Unleashing a Materials Revolution

Thomas Edison's painstaking trial-and-error approach to invention has served as the model for generations of innovators in all scientific disciplines.  In the late 1870s, according to the Smithsonian, Edison compiled more than 40,000 pages of notes as he tried to find the right fiber for the filament of the electric light bulb.

One at a time, he tested 1,600 different materials, including coconut fiber, fishing line, cardboard, and hair from a colleague's beard, before finally achieving success with carbonized bamboo fiber.

Over the past century-and-a-half, that time-consuming, expensive approach has continued to be the standard method for scientists seeking the right material for a new application.

In fact, according to MIT Professor of Materials Science and Engineering Gerbrand Ceder and Kristin Persson, a staff scientist at Lawrence Berkeley National Laboratory, companies spend billions of dollars using guesswork and trial and error to develop new materials.1  Along the way, scientists spend months at a time following dead ends, and the vast majority of potential materials never make it out of the lab.

Even when companies succeed, it takes an average of 15 to 20 years to develop a new material.  For example, Sony's commercialization of the lithium-ion battery took place only after hundreds of researchers devoted almost two decades to the effort.

But now, the Digital Techno-Economic Revolution has opened up a dramatically faster, more efficient way to handle the task of developing new materials, and the implications will be profound in virtually every area of science and everyday life.

To grasp how much has changed, consider what it would have meant to Edison if he had today's supercomputer power in his Menlo Park laboratory.  Instead of wasting months trying to identify every known type of fiber and then testing each one individually within a prototype, all that Edison would have had to do was plug the parameters he was seeking into a database of materials.

For example, he would have specified "low cost," "ability to withstand high temperatures," and "high resistance" so the fiber would use less electricity. Within seconds, he would have seen a list of the existing materials with the exact properties he needed.

Better yet, all of the potential combinations of the compounds that make up those materials would be evaluated against the criteria as well, meaning that he would have been able to design an even more efficient new material at the atomic level — and he could have done all this before he had to conduct a single test on a prototype...

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