New AI method captures long-range atomic interactions in complex molecules
Researchers from Google DeepMind in Berlin, BIFOLD, and the Technical University of Berlin have introduced a new machine learning method—Euclidean Fast Attention (EFA)—that enables global atomic interactions in chemical systems to be represented more efficiently. This could allow chemical and materi...
Image: Phys.org
Researchers from Google DeepMind in Berlin, BIFOLD, and the Technical University of Berlin have introduced a new machine learning method—Euclidean Fast Attention (EFA)—that enables global atomic interactions in chemical systems to be represented more efficiently. This could allow chemical and materials science processes to be simulated more accurately in the future, potentially accelerating the development of new drugs, more efficient batteries, and more sustainable materials.
Originally published at Phys.org
