Developed at NIST with the University of Maryland, the algorithm fires X-ray beams at a tin-bismuth wafer and extrapolates beyond the already measured points to decide where to look next.
A wafer on which the ratio of two elements has been continuously varied contains, in practice, several hundred thousand distinct materials — one for each measurable point. Add temperature as a variable, and the space to explore becomes virtually endless. This is the problem tackled by AMASE, or Autonomous Materials Search Engine, developed by NIST researcher Gilad Kusne with colleagues at the University of Maryland.
The algorithm fires X-ray beams at the wafer — tin and bismuth in the published study — and characterizes the materials according to their composition and temperature. Its particularity lies in its ability to extrapolate: rather than limiting itself to interpolating between points already measured, it makes predictions beyond the available dataset, and chooses the following measurements on this basis.
The targeted applications are automated manufacturing and the search for quantum materials, two fields where the cost of an exhaustive experimental campaign prohibits any other than guided sampling.
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