While the high efficiency of Density Functional Theory (DFT) calculations has enabled many important materials science application over the past decades, modern scientific problems require accurate electronic structure data beyond the scales attainable with DFT. For instance, the modeling of materials at extreme conditions across multiple length and time scales, which is important for the understanding for physical phenomena such as radiation damages in fusion reactor walls, evades ab-initio treatment.
One possible method to obtain such models at near ab-initio accuracy are DFT surrogate models, that, based on machine learning (ML) algorithms, reproduce DFT results at a fraction of the cost. One drawback of the ML workflow is the need for hyperparameter optimization, i.e., the need to tune the employed ML algorithm in order to best perform on the given dataset. Manually performing this optimization becomes prohibitive if a wide range of materials and conditions is eventually to be treated. Here, we present results of an hyperparameter study in an effort to find optimal surrogate models for aluminium at ambient conditions 1, that investigates how modern hyperparameter optimization techniques can be used to automate large parts of the model selection process and eventually move towards automated surrogate model creation. The models are based upon the Materials Learning Algorithms (MALA) package 2 and the therein implemented LDOS based machine learning workflow 3.