Modelling local and general quantum mechanical properties with attention-based pooling.

ABSTRACT: Atom-centred neural networks represent the state-of-the-art for approximating the quantum chemical properties of molecules, such as internal energies. While the design of machine learning architectures that respect chemical principles has continued to advance, the final atom pooling operation that is necessary to convert from atomic to molecular representations in most models remains relatively undeveloped. The most common choices, sum and average pooling, compute molecular representations that are naturally a good fit for many physical properties, while satisfying properties such as permutation invariance which are desirable from a geometric deep learning perspective. However, there are growing concerns that such simplistic functions might have limited representational power, while also b

SUBMITTER: Buterez D 

PROVIDER: S-EPMC10686994 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature