# Learning representations of molecules and materials with atomistic neural networks

## TL;DR

This paper introduces neural network architectures, especially SchNet, that learn efficient, chemically intuitive representations of molecules and materials, accurately predicting properties across diverse datasets.

## Contribution

The paper presents SchNet, a continuous-filter convolutional neural network that effectively models molecules and materials, demonstrating its ability to predict properties and analyze learned representations.

## Key findings

- SchNet accurately predicts chemical properties across datasets
- Learned representations align with chemical intuition
- Neural networks can model complex molecular structures

## Abstract

Deep Learning has been shown to learn efficient representations for structured data such as image, text or audio. In this chapter, we present neural network architectures that are able to learn efficient representations of molecules and materials. In particular, the continuous-filter convolutional network SchNet accurately predicts chemical properties across compositional and configurational space on a variety of datasets. Beyond that, we analyze the obtained representations to find evidence that their spatial and chemical properties agree with chemical intuition.