QT-Net: Rethinking Evaluation of AI Models in Atomic Chemical Space

TL;DR

This paper introduces QT-Net, a novel neural network for predicting atomic properties with a new evaluation protocol, demonstrating improved atomic and molecular property predictions and providing open-source code for broader use.

Contribution

The work presents a new evaluation protocol for atomic properties and introduces QT-Net, a rotationally augmented neural network that improves atomic and molecular property predictions.

Findings

QT-Net outperforms non-equivariant models in atomic property prediction.

The evaluation protocol effectively assesses out-of-distribution atomic environments.

QT-Net's atomic property predictions enhance downstream molecular property tasks.

Abstract

Atomic properties such as partial charges or multipoles encode chemically meaningful information that can inform downstream molecular property prediction, but their evaluation as machine learning targets has been complicated by the absence of a principled out-of-distribution evaluation protocol at the atomic level. In this work, we propose a held-out evaluation protocol that clusters atomic environments by SOAP descriptors and computes metrics accounting only for cluster labels unseen during training. Following this procedure, we use 5$\times$5 cross-validation and Tukey's HSD to run a statistically rigorous comparison of E(3)-equivariant against non-equivariant, rotationally augmented models for predicting electron populations and multipoles of H, C, N, and O atoms. Building on our results, we introduce the Quantum Topological Neural Network (QT-Net), a rotationally augmented, non-equivariant graph neural network. We show that QT-Net can be used to infer properties of atoms in molecules from QM9 outside our training set, and that these inferred properties can yield improvement when used as input features for downstream molecular property prediction. To further validate the framework, molecular dipole moments computed from QT-Net's per-atom outputs recover the ground-truth values reported in QM9. We release all code and data, including a JAX implementation of QT-Net, to support the broader use of learned QTA properties as inductive biases for atomic-scale molecular machine learning.