Deep learning optimal molecular scintillators for dark matter direct detection

TL;DR

This paper introduces a machine learning approach using neural networks to generate and identify novel organic molecules with properties optimized for dark matter detection, overcoming database limitations.

Contribution

It presents a neural network framework combining a variational autoencoder and perceptron to efficiently generate molecules with targeted properties for dark matter detectors.

Findings

Generated molecules not present in existing databases.

Identified promising molecular structures through clustering analysis.

Demonstrated effective property prediction for high-throughput screening.

Abstract

Direct searches for sub-GeV dark matter are limited by the intrinsic quantum properties of the target material. In this proof-of-concept study, we argue that this problem is particularly well suited for machine learning. We demonstrate that a simple neural architecture consisting of a variational autoencoder and a multi-layer perceptron can efficiently generate unique molecules with desired properties. In specific, the energy threshold and signal (quantum) efficiency determine the minimum mass and cross section to which a detector can be sensitive. Organic molecules present a particularly interesting class of materials with intrinsically anisotropic electronic responses and $\mathcal{O}$(few) eV excitation energies. However, the space of possible organic compounds is intractably large, which makes traditional database screening challenging. We adopt excitation energies and proxy transition matrix elements as target properties learned by our network. Our model is able to generate molecules that are not in even the most expansive quantum chemistry databases and predict their relevant properties for high-throughput and efficient screening. Following a massive generation of novel molecules, we use clustering analysis to identify some of the most promising molecular structures that optimise the desired molecular properties for dark matter detection.