Multi-Task Fine-Tuning Enables Robust Out-of-Distribution Generalization in Atomistic Models 1. A new study proposes multi-task fine-tuning (MFT) to enhance the out-of-distribution (OOD) generalization of atomistic models, addressing a critical issue in molecular and materials design where models often fail to generalize beyond known data regimes. 2. The research identifies a key problem: standard fine-tuning of pretrained models leads to "representation collapse," erasing crucial chemical and structural priors learned during pretraining and severely degrading OOD performance. 3. MFT jointly optimizes downstream property prediction with a physically grounded force-field objective inherited from pretraining, preserving essential chemical priors while allowing task-specific adaptation. 4. Across various molecular and materials benchmarks, MFT consistently improves OOD generalization, approaching the theoretical limit set by in-distribution accuracy and outperforming standard fine-tuning and state-of-the-art task-specific models. 5. The study demonstrates that MFT mitigates representation collapse, maintaining clear separation of atomic and edge representations, which is crucial for robust OOD performance. 6. MFT also enhances data efficiency, achieving strong OOD performance even with limited training data, making it highly practical for real-world applications where labeled data is scarce. 7. The findings highlight the importance of balancing representation stability and plasticity during fine-tuning, offering insights for designing next-generation atomistic models for reliable scientific discovery. 📜Paper: arxiv.org/abs/2601.08486v1 #AtomisticModels #MultiTaskFineTuning #OODGeneralization #MolecularDesign #MaterialsScience