Cluster characterization in atom probe tomography: Machine learning using multiple summary functions

In this work, we develop a machine learning-based method to characterize intracluster concentration (ρc), background concentration (ρb), clustering radius (r̄), and radius dispersity (δr) in simulated atom probe tomography data using multiple spatial statistics summary functions to train a Bayesian regularized neural network. We build upon previous work that utilized Ripley’s K-function by incorporating additional features from nearest-neighbor spatial statistics summary functions to better characterize concentration-based metrics. The addition of nearest-neighbor based features allows for highly accurate estimates of ρc and ρb, both with 90% of the predictions within 4.0% of the real value; the root-mean-square errors are reduced by 81.5% and 92.8% from predictions using only K-function based features, respectively. Additionally, including these nearest-neighbor based features improves the ability to differentiate between r̄ and δr. •Here, we use statistical learning to characterize simulated clustered atom probe tomography data.•Nearest-neighbor functions significantly improve performance of machine learning algorithm.•81% reduction in the error of prediction of the concentration in the clusters.•92% reduction in the error of prediction of the concentration in the background.•New ability to differentiate variations in radius from changes in the average.