Figure 1: Our adaptive design loop.

(a) Prior knowledge, including data from previous experiments and physical models, and relevant features are used to describe the materials. This information is used within a machine learning framework to make predictions that include error estimates. The results are used by an experimental design tool (for example, Global optimization) that suggests new experiments (synthesis and characterization) performed in this work, with the dual goals of model improvement and materials discovery. The results feed into a database, which provides input for the next iteration of the design loop. The green arrows represent the step-wise approach of the state-of-art using experiments or calculations, although few studies have demonstrated feedback. The red star shows that although sample number 3 is not the best predicted choice relative to sample 4, the ‘expected improvement’ by selecting it is greater than other choices due to the large uncertainty. (b) Our loop, as executed in practice specific to the design problem featured in this work, is as follows: (i) an initial alloy experimental data set with known thermal dissipation ΔT and features or materials descriptors serves as input to the inference model. (ii) The model is trained and cross-validated with the initial alloy data. (iii) A data set of unexplored alloys defines the total search space of probable candidates. The trained model in (ii) is applied to all the alloys in (iii), to predict their ΔT. (iv) The design chooses the ‘best’ four candidates for synthesis and characterization. (v) The new alloys, with their measured ΔT, augment the initial data set to further improve the inference and design. The four alloys for experiments are chosen iteratively by augmenting four times the initial data set with each new predicted alloy from the inference and design.