ElectroLens: Understanding Atomistic Simulations Through Spatially-resolved Visualization of High-dimensional Features
This addresses a bottleneck for researchers in chemical informatics by providing a tool to visualize and connect features, though it is incremental as it builds on existing visualization methods.
The paper tackles the problem of interpreting abstract high-dimensional features in atomistic simulations by introducing ElectroLens, a visualization tool that links features to 3D chemical systems through interactive views, enabling better diagnosis and intuition.
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.