Accelerated search for materials with targeted properties by adaptive design

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 (Δ T ) NiTi-based shape memory alloys, with Ti 50.0 Ni 46.7 Cu 0.8 Fe 2.3 Pd 0.2 possessing the smallest Δ T (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 Δ T than any of the 22 in the original data set.