Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learning

TL;DR

This paper introduces a physics-informed consistency training method that enables multi-task learning models to effectively integrate heterogeneous molecular data, improving predictions by leveraging physical laws and cross-task information exchange.

Contribution

It proposes a novel consistency training approach that exploits physical laws to connect different molecular tasks, enhancing multi-task learning with heterogeneous data.

Findings

Energy data improves structure prediction accuracy.

Force and off-equilibrium data enhance structure prediction.

Physical consistency enables better data integration.

Abstract

In recent years, machine learning has demonstrated impressive capability in handling molecular science tasks. To support various molecular properties at scale, machine learning models are trained in the multi-task learning paradigm. Nevertheless, data of different molecular properties are often not aligned: some quantities, e.g. equilibrium structure, demand more cost to compute than others, e.g. energy, so their data are often generated by cheaper computational methods at the cost of lower accuracy, which cannot be directly overcome through multi-task learning. Moreover, it is not straightforward to leverage abundant data of other tasks to benefit a particular task. To handle such data heterogeneity challenges, we exploit the specialty of molecular tasks that there are physical laws connecting them, and design consistency training approaches that allow different tasks to exchange information directly so as to improve one another. Particularly, we demonstrate that the more accurate energy data can improve the accuracy of structure prediction. We also find that consistency training can directly leverage force and off-equilibrium structure data to improve structure prediction, demonstrating a broad capability for integrating heterogeneous data.