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Abstract
This research proposes an improved machine learning framework for simulating open-shell anions and cations using the AIMNet-NSE (Neural Spin Equilibration) architecture. This architecture predicts molecular energies for various charge and spin multiplicities with high accuracy (around 2–3 kcal/mol error for energy and 0.01e for spin-charges). It bypasses QM calculations to derive ionization potential, electron affinity, and conceptual DFT quantities, enabling chemical reactivity modeling.
Publisher
Nature Communications
Published On
Aug 11, 2021
Authors
Roman Zubatyuk, Justin S. Smith, Benjamin T. Nebgen, Sergei Tretiak, Olexandr Isayev
Tags
machine learning
open-shell ions
AIMNet-NSE
chemical reactivity
ionization potential
electron affinity
DFT quantities

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