Machine learning of properties of lead-free perovskites with a neural network with additive kernel regression-based neuron activation functions

Machine learning (ML) of properties of perovskite materials, in particular of the bandgap of perovskites used in optoelectronic applications, has recently attracted increasing attention. Typically, off-the-shelf ML methods such as neural networks (NN), kernel methods or tree-based methods are used. We employ the recently proposed type of NN that uses additive Gaussian process regression to construct optimal neuron activation functions and avoids non-linear optimization to machine learn the band gap and heat of formation of lead-free inorganic halide double perovskites for solar cell applications. The method combines the high expressive power of an NN with the robustness of a linear regression. We show that better prediction quality can be obtained, in particular in the visible region relevant for most applications, compared to previous results using standard methods. Most important variables and the importance of coupling among features, in particular for bandgap prediction, can also be identified with the new method.