The
band gap is an important parameter that determines light-harvesting
capability of perovskite materials. It governs the performance of
various optoelectronic devices such as solar cells, light-emitting
diodes, and photodetectors. For perovskites of a formula ABX3 having a non-zero band gap, we study nonlinear mappings between
the band gap and properties of constituent elements (e.g., electronegativities,
electron affinities, etc) using alternating conditional expectations
(ACE)a machine learning technique suitable for small data
sets. We also compare ACE with other machine learning methods: decision
trees, kernel ridge regression, extremely randomized trees, AdaBoost,
and gradient boosting. The best performance is achieved by kernel
ridge regression and extremely randomized trees. However, ACE has
an advantage that it presents its results in a graphic form, helping
in interpretation. The models are trained with the data obtained from
density functional theory calculations. Different statistical approaches
for feature selection are applied and compared: Pearson correlation,
Spearman’s rank correlation, maximal information coefficient,
distance correlation, and ACE. A classification task of separating
metallic perovskites from nonmetallic ones is solved using support-vector
machines with the radial basis function kernel.