Poster
in
Workshop: From Molecules to Materials: ICLR 2023 Workshop on Machine learning for materials (ML4Materials)

3D Graph Conditional Distributions via Semi-Equivariant Continuous Normalizing Flows

Eyal Rozenberg · Ehud Rivlin · Daniel Freedman


Abstract: A general method for learning the conditional distribution $p(G | \hat{G})$ of two 3D graphs is proposed. The method is designed to be invariant to rigid body transformations and to permutations of the vertices of either graph. The core of the method is a continuous normalizing flow and semi-equivariance conditions are established to ensure the aforementioned invariance conditions. The utility of the technique is demonstrated as a conditional generative model for the molecular setting.

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