A key aspect of the developing field of materials informatics is optimally guiding experiments or calculations towards parts of the relatively vast feature space where a material with desired property may be discovered. We discuss our approach to adaptive experimental design and the methods developed in decision theory and global optimization which can be used in materials science. We show that the use of uncertainties to trade-off exploration versus exploitation to guide new experiments or calculations generally leads to enhanced performance, highlighting the need to evaluate and incorporate errors in predictive materials design. We illustrate our ideas on a computed data set of M₂AX phases generated using ab initio calculations to find the sample with the optimal elastic properties, and discuss how our approach leads to the discovery of new NiTi-based alloys with the smallest thermal dissipation.

材料信息学发展领域的一个关键方面是对可能发现具有所需特性的材料的相对广阔的特征空间中的部分进行优化的实验或计算指导。我们讨论了自适应实验设计的方法以及在决策理论和全局优化中开发的方法，这些方法可用于材料科学。我们表明，使用不确定性权衡探索与开发之间的权衡关系以指导新的实验或计算通常会提高性能，突出表明需要评估并在预测性材料设计中纳入错误。我们将说明从头算生成的M ₂ AX相的计算数据集的思想 计算以找到具有最佳弹性性能的样品，并讨论我们的方法如何导致发现具有最小热耗散的新型NiTi基合金。