Journal
2019 IEEE VISUALIZATION CONFERENCE (VIS)
Volume -, Issue -, Pages 196-200Publisher
IEEE
DOI: 10.1109/visual.2019.8933647
Keywords
Molecular Visualization; Visual Design; Coordinated and Multiple Views; Interaction Design
Categories
Funding
- U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences Computational Chemical Sciences program [DE-SC0019410]
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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.
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