Recently, several equivariant message passing neural networks (MPNNs) have been shown to outperform models built using otherapproaches intermsofaccuracy.

Retrosynthetic planning occupies a crucial position in synthetic chemistry and, accordingly, drug discovery, which aims to find synthetic pathways of a target molecule through a sequential decision-making process on a set of feasible reactions.

In density functional theory, the charge density is the core attribute of atomic systems from which all chemical properties can be derived.

To address this, we leverage parametric modular structure to learn component-level surrogates, enabling cheaper high-fidelity simulation. We use a neural network to model the stored potential energy in a component given boundary conditions.

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