Accelerated search for materials with targeted properties by adaptive design

Dezhen Xue, Prasanna V. Balachandran, John Hogden, James Theiler, Deqing Xue and Turab Lookman ()
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Dezhen Xue: Los Alamos National Laboratory
Prasanna V. Balachandran: Los Alamos National Laboratory
John Hogden: Computer and Computational Sciences, Los Alamos National Laboratory
James Theiler: Intelligence and Space Research, Los Alamos National Laboratory
Deqing Xue: State Key Laboratory for Mechanical Behavior of Materials, Xi’an Jiaotong University
Turab Lookman: Los Alamos National Laboratory

Nature Communications, 2016, vol. 7, issue 1, 1-9

Abstract: Abstract Finding new materials with targeted properties has traditionally been guided by intuition, and trial and error. With increasing chemical complexity, the combinatorial possibilities are too large for an Edisonian approach to be practical. Here we show how an adaptive design strategy, tightly coupled with experiments, can accelerate the discovery process by sequentially identifying the next experiments or calculations, to effectively navigate the complex search space. Our strategy uses inference and global optimization to balance the trade-off between exploitation and exploration of the search space. We demonstrate this by finding very low thermal hysteresis (ΔT) NiTi-based shape memory alloys, with Ti50.0Ni46.7Cu0.8Fe2.3Pd0.2 possessing the smallest ΔT (1.84 K). We synthesize and characterize 36 predicted compositions (9 feedback loops) from a potential space of ∼800,000 compositions. Of these, 14 had smaller ΔT than any of the 22 in the original data set.

Date: 2016
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DOI: 10.1038/ncomms11241

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