From Independent to Context-Aware Inference via Tabular Foundation Models for Energy Prediction on 3D Atomic Structures

Machine Learning Interatomic Potentials (MLIPs) provide an efficient approach to approximating Density Functional Theory (DFT) energy calculations on 3D atomic structures. However, existing MLIPs largely follow an independent inference paradigm, leaving previously computed DFT observations unused and limiting potential performance gains from such observations. We introduce In-Context MLIPs (IC-MLIPs), a new inference paradigm that augments MLIPs with In-Context Learning (ICL) for context-aware energy prediction. Specifically, we attach Tabular Foundation Models (TFMs) as contextual predictors on top of pretrained MLIPs, conditioning predictions on atomic structures with previously computed energy targets without additional training or fine-tuning of the base MLIPs. Experiments across six large-scale datasets spanning diverse domains demonstrate substantial performance gains from ICL-based inference. To address the unique challenges arising when applying TFMs to IC-MLIPs, we further introduce Structure-Global Information Enrichment (SGIE) to enrich atom-wise MLIP representations with structure-global information, and Dynamic Context Construction to adaptively construct effective contexts during TFMs training. Together, these components yield further performance improvements.