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Orbital-free Bond Breaking via Machine Learning
Machine learning is used to approximate the kinetic energy of one dimensional diatomics as a functional of the electron density.
Learning Invariant Representations of Molecules for Atomization Energy Prediction
The accurate prediction of molecular energetics in chemical compound space is a crucial ingredient for rational compound design.
How to Explain Individual Classification Decisions
After building a classifier with modern tools of machine learning we typically have a black box at hand that is able to predict well for unseen data.
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