We also use AL with various acquisition functions to strategically select which materials and pressure points among a large pool of unlabeled candidates to evaluate next. We denote the current training dataset as {𝑥 𝑖 , 𝑦 𝑖 𝑖=1 𝑛 } and the unlabeled candidate pool as 𝑈 = {𝑥 𝑛+1, 𝑥 𝑛+2, . . . }. The initial training dataset is gotten from randomly selected two data points across the entire dataset. At each iteration of AL, we: