Coarse-Graining with Equivariant Neural Networks: A Path Toward Accurate and Data-Efficient Models.

ABSTRACT: Machine learning has recently entered into the mainstream of coarse-grained (CG) molecular modeling and simulation. While a variety of methods for incorporating deep learning into these models exist, many of them involve training neural networks to act directly as the CG force field. This has several benefits of which the most significant is accuracy. Neural networks can inherently incorporate multibody effects during the calculation of CG forces, and a well-trained neural network force field outperforms pairwise basis sets generated from essentially any methodology. However, this comes at a significant cost. First, these models are typically slower than pairwise force fields, even when accounting for specialized hardware, which accelerates the training and integration of such networks. Th

SUBMITTER: Loose TD 

PROVIDER: S-EPMC10726966 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature