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Contributors | ? |
Other stakeholders | David Mobley , Michael Gilson , Michael Shirts , Daniel Cole |
Objective | A neural network charge model that can assign conformer-independent charges to both small molecules and large systems, at a higher level of theory than AM1BCC |
Time frame | ? |
Key outcomes | A neural network charge model that:
A force field incorporating:
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Status | IN PROGRESS |
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\uD83E\uDD14 Problem Statement and Objective
AM1-BCC charges are trained to reproduce RESP charges, which are calculated at a low level of QM theory (HF/6-31G*) and rely on that theory level’s overpolarization to fortuitously model charge polarization in solution. The level of theory is particularly poorly suited for sulfur and phosphorus, which can be hypervalent, as well as some other functional groups. Additionally, it has been shown that HF/6-31G* does not consistently overpolarize charges by the same amount in every system, and within a given system, it erroneously polarizes both solvent-accessible and buried atoms by the same amount. These issues with polarization become more problematic the larger the simulated system is, causing more problems for large systems than small molecules.
In order to accurately model electrostatics, we wish to train a graph neural network charge model which solves these problems. We will train the GNN to a higher level of QM theory, to more accurately capture the electrostatics of complicated systems like hypervalent atoms. We will model the effects of solvent polarization directly by using a solvent model.
🎯 Scope
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