Incorporating Neural Networks into the AMOEBA Polarizable Force Field

17 November 2023, Version 2
This content is a preprint and has not undergone peer review at the time of posting.


Neural network potentials (NNPs) have great potential to bridge the gap between the accuracy of quantum mechanics and the efficiency of molecular mechanics in molecular simulation. However, most of the NNPs remain restricted by the locality assumption that ensures the model's transferability and scalability but misses out the long-range interactions. Here we present an integrated non-reactive hybrid model, AMOEBA+NN, which employs the AMOEBA potential for the short- and long-range non-bonded interactions and an NNP to capture the remaining local (covalent) contributions. A first AMOEBA+NN model was trained on the conformational energy of ANI-1x dataset and tested on several external datasets ranging from small molecules to tetrapeptides. It was encouraging to see that the hybrid model becomes significantly advantageous over the baseline models in term of accuracy as the molecules get larger, offering perspectives for the development of a generalized and improved approach.


Neural Network Potentials
Polarizable Force Fields
Machine Learning
Deep Learning


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