Impact of nanoparticle morphologies on property prediction using explainable AI

Every decision made during a machine learning pipeline has an impact on the outcome. Feature selection can reduce overfitting and focus models on the attributes that matter most, and sample selection can reduce bias to ensure models recognise patterns comprehensively. eXplainable AI (XAI) can provide quantitative ways of evaluating the impact of these decisions, and help ensure the right data is used for training models predicting structure property relationships. In this paper we explore the use of residual decomposition with Shapely values to identify which nanoparticle shapes are most influential in predicting charge transfer properties of gold nanoparticles and how they impact the ability to predict the properties of the different morphologies.