Prototype-Guided Latent Alignment for Data-Efficient Fine-Tuning of Molecular Foundation Models

Abstract: Machine learning interatomic potentials (MLIPs) have transformed materials discovery by leveraging graph neural networks (GNNs) to predict material properties with near density functional theory (DFT) accuracy. While large-scale pretrained foundation models offer transferable baseline representations, they frequently struggle to generalise to out-of-distribution (OOD) target systems -- a common challenge in modelling complex or chemically diverse materials. Fine-tuning is the standard remedy, but the high cost of generating DFT-labelled configurations confines adaptation to data-scarce regimes, where over-parameterised GNNs amplify overfitting and degrade target-domain performance. To address this, we propose a prototype-based alignment approach for data-efficient fine-tuning of MLIPs. Our method identifies local structural similarities between the source and target domains by grouping atoms with analogous chemical environments based on their latent representations. Each target-domain atom's energy contribution is aligned to its source-domain prototype, introducing an inductive bias that anchors fine-tuned representations to the pretrained structure, encouraging effective reuse of learned interactions and improving generalisation without restrictive assumptions on the target chemistry. We evaluate our method on the rMD17 benchmark using equivariant MACE and invariant SchNet across varying data budgets, and extend evaluation to the MACE-OFF foundation models on the SPICE dataset. Our approach consistently improves predictive accuracy in the low-data regime, reducing energy MAE by up to 18% over standard fine-tuning baselines.