The field of machine learning for molecular interactions is shifting towards more innovative and principled approaches. Researchers are exploring new formulations, such as categorical representations of energy, to quantify model uncertainty and improve performance. Another significant direction is the development of multimodal benchmarks, which enable the learning of transferable quantum interactions through coupled visual and numerical modalities. Furthermore, studies have shown that scientific models learn similar internal representations of matter, indicating a promising path towards building reliable foundation models. Noteworthy papers include:

• From Regression to Classification: Exploring the Benefits of Categorical Representations of Energy in MLIPs, which demonstrates the potential of categorical formulations in achieving comparable performance to regression baselines while quantifying model uncertainty.
• QuantumCanvas: A Multimodal Benchmark for Visual Learning of Atomic Interactions, which introduces a large-scale multimodal benchmark for learning transferable quantum interactions.
• Universally Converging Representations of Matter Across Scientific Foundation Models, which shows that representations learned by different models are highly aligned across various chemical systems.
• The Universal Weight Subspace Hypothesis, which identifies universal subspaces capturing majority variance in deep neural networks, offering insights into the intrinsic organization of information within deep networks.