Xue Dezhen, Balachandran Prasanna V, Hogden John, Theiler James, Xue Deqing, Lookman Turab
Theoretical Division, Los Alamos National Laboratory, MS-B262, Los Alamos, New Mexico 87545, USA.
State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an 710049, China.
Nat Commun. 2016 Apr 15;7:11241. doi: 10.1038/ncomms11241.
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.
传统上，寻找具有特定性能的新材料是靠直觉和反复试验来指导的。随着化学复杂性的增加，组合可能性太多，爱迪生式的方法已不切实际。在此，我们展示了一种与实验紧密结合的自适应设计策略如何通过依次确定下一个实验或计算，有效地在复杂搜索空间中导航，从而加速发现过程。我们的策略使用推理和全局优化来平衡在搜索空间的利用和探索之间的权衡。我们通过找到热滞（ΔT）极低的镍钛基形状记忆合金来证明这一点，其中Ti50.0Ni46.7Cu0.8Fe2.3Pd0.2的ΔT最小（1.84 K）。我们从约800,000种成分的潜在空间中合成并表征了36种预测成分（9个反馈循环）。其中，有14种的ΔT比原始数据集中的22种中的任何一种都小。
