High-throughput Quantum Theory of Atoms in Molecules (QTAIM) for Geometric Deep Learning of Molecular and Reaction Properties
Santiago Vargas,
Winston Gee,
Anastassia Alexandrova
Abstract:We present a package, Generator, for geometric molecular property prediction based on topological features of quantum mechanical electron density. Generator computes Quantum Theory of Atoms in Molecules (QTAIM) features, at density functional theory (DFT) level, for sets of molecules or reac- tions in a high-throughput manner, and compiles features into a single data structure for processing, analysis, and geometric machine learning. An accompanying graph neural network package can be used for property predict… Show more
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