  In recent years, machine learning (ML) has gained significant popularity in
the field of chemical informatics and electronic structure theory. These
techniques often require researchers to engineer abstract "features" that
encode chemical concepts into a mathematical form compatible with the input to
machine-learning models. However, there is no existing tool to connect these
abstract features back to the actual chemical system, making it difficult to
diagnose failures and to build intuition about the meaning of the features. We
present ElectroLens, a new visualization tool for high-dimensional
spatially-resolved features to tackle this problem. The tool visualizes
high-dimensional data sets for atomistic and electron environment features by a
series of linked 3D views and 2D plots. The tool is able to connect different
derived features and their corresponding regions in 3D via interactive
selection. It is built to be scalable, and integrate with existing
infrastructure.
