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Article overview
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Graph Neural Network with Local Frame for Molecular Potential Energy Surface | | Xiyuan Wang
; Muhan Zhang
; | | Date: |
1 Aug 2022 | | Abstract: | Modeling molecular potential energy surface is of pivotal importance in
science. Graph Neural Networks have shown great success in this field,
especially those using rotation-equivariant representations. However, they
either suffer from a complex mathematical form or lack theoretical support and
design principle. To avoid using equivariant representations, we introduce a
novel local frame method to molecule representation learning and analyze its
expressive power. With a frame and the projection of equivariant vectors on the
frame, GNNs can map the local environment of an atom to a scalar representation
injectively. Messages can also be passed across local environments with frames’
projection on frames. We further analyze when and how we can build such local
frames. We prove that local frames always exist when the local environments
have no symmetry, as is often the case in molecular dynamics simulations. For
symmetric molecules, though only degenerate frames can be built, we find that
the local frame method may still achieve high expressive power in some common
cases due to the reduced degrees of freedom. Using only scalar representations
allows us to adopt existing simple and powerful GNN architectures. Our model
outperforms a range of state-of-the-art baselines in experiments. Simpler
architectures also lead to higher scalability. Our model only takes about 30%
inference time compared with the fastest baseline. | | Source: | arXiv, 2208.00716 | | Services: | Forum | Review | PDF | Favorites | |
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