Work

Deep learning optimal molecular scintillators for dark matter direct detection

Year: 2025

Type: article

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 multilayer 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 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 optimize the desired molecular properties for dark matter detection.

Source: Physical review. D/Physical review. D.

DOI: 10.1103/89gh-lwcd

Authors: Cameron Cook, Carlos Blanco, Juri Smirnov

Institutions: University of Liverpool, Stockholm University, Princeton University

Language: English

Topic: Dark Matter and Cosmic Phenomena

Subfield: Nuclear and High Energy Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: hybrid