Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learning

Understand

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.