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“This work is an illustration of how recent advances in seemingly distant fields such as material physics, artificial intelligence, computing, and machine learning can be brought together to advance scientific knowledge that has strong implications for industry application,” Suresh says.
The new method, the researchers say, could open up possibilities for creating materials tuned precisely for electronic, optoelectronic, and photonic devices that could find uses for communications, information processing, and energy applications.
Image caption: When a small amount of strain is applied to a crystalline material like silicon, its properties can change dramatically; for example, it can shift from blocking electrical current to conducting it freely like a metal. Credit: Frank Shi
The team studied the effects of strain on the bandgap, a key electronic property of semiconductors, in both silicon and diamond. Using their neural network algorithm, they were able to predict with high accuracy how different amounts and orientations of strain would affect the bandgap.
“Tuning” of a bandgap can be a key tool for improving the efficiency of a device, such as a silicon solar cell, by getting it to match more precisely the kind of energy source that it is designed to harness. By fine-tuning its bandgap, for example, it may be possible to make a silicon solar cell that is just as effective at capturing sunlight as its counterparts but is only one-thousandth as thick. In theory, the material “can even change from a semiconductor to a metal, and that would have many applications, if that’s doable in a mass-produced product,” Li says.
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