A shared-weight neural network architecture for predicting molecular properties

Abstract

Quantum chemical methods scale poorly with increasing molecular size and machine learning models have emerged as a promising, computationally-efficient alternative. We present a shared-weight neural network architecture based on modified atom-centered symmetry functions (ACSFs) and show that it performs similarly to the more computationally expensive per-element neural networks of previous work with ACSFs. The model achieves chemically accurate predictions, with a mean absolute error as low as 0.63 kcal mol⁻¹ on energy predictions in the QM9 data set. Additionally, we show that it can reliably predict atomic forces.