Researchers from Google DeepMind Berlin, BIFOLD, and the Technical University of Berlin have introduced a new machine learning method called Euclidean Fast Attention, or EFA. Published in Nature Machine Intelligence in March 2026, this technique enables the efficient representation of global atomic interactions in chemical systems. The breakthrough addresses a major computational bottleneck in chemistry and materials science, potentially accelerating the development of new drugs, better batteries, and sustainable materials. Simulating molecular behavior is critical for understanding how drugs function or how new materials behave. However, traditional computational methods struggle with large molecules containing hundreds or thousands of atoms. This difficulty arises because each atom experiences forces from distant neighbors, not just those immediately adjacent. Consequently, small changes in one location can influence the entire system, creating a complex many-body environment that is hard to model accurately. Modern machine learning often relies on self-attention mechanisms to capture long-range relationships. While effective, this approach scales poorly as systems grow. The number of relevant interactions increases approximately with the square of the number of atoms, making precise modeling of large physical systems prohibitively expensive in terms of computing power. This limitation restricts the size of atomistic structures that can be simulated. EFA overcomes this challenge by introducing a linearly scaling representation designed for data in Euclidean space, where classical geometry rules apply. This spatial framework is essential for modeling atoms in molecules, where relative positions and orientations are crucial for accuracy. A key innovation of EFA is its ability to represent spatial information efficiently while preserving important physical symmetries. Experiments demonstrated that EFA effectively captures various long-range effects that conventional machine-learning force fields often misinterpret. The method reliably models interactions over large distances while requiring significantly less computational effort. This allows for quantum-mechanically accurate modeling of many-body systems using deep learning without sacrificing efficiency. Prof. Klaus-Robert Müller, co-director of BIFOLD and a professor at the Technical University of Berlin, emphasized that this approach represents a significant step forward. It resolves the central question of how to incorporate global structural information into atomistic models while maintaining the speed necessary for large-scale simulations. Because EFA is optimized for large molecules, it is poised to handle demanding applications in complex materials science. The authors describe EFA as a robust and efficient tool for tackling challenging simulations in chemistry and physics. By enabling the study of larger and more complex systems, this technology could transform how scientists design new chemical processes and materials. The research provides a foundation for future innovations that require high-fidelity atomic modeling without the traditional computational overhead.