Transfer learning and few-shot force-field learning

Determine the degree of fine-tuning required for downstream tasks in molecular foundation models and establish whether few-shot force-field learning is feasible with fewer than 100 training examples.

Background

The paper explicitly queries the transferability of pretrained molecular foundation models to downstream tasks such as force-field learning, especially under data scarcity. Understanding how much fine-tuning is needed and whether few-shot regimes are viable is crucial for practical adoption.

The authors highlight the potential for substantial efficiency gains if robust few-shot learning of physically consistent force fields can be achieved.