General Framework for Geometric Deep Learning on Tensorial Properties of Molecules and Crystals
Understanding structure–property relationships has long been a central theme in theoretical studies of physics, chemistry, and materials science. With the rapid development of artificial intelligence and deep learning techniques in scientific computing, data-driven machine learning models have demonstrated remarkable success in efficiently predicting scalar physicochemical properties of atomistic systems, such as energies and charges, thereby greatly expanding the accessible chemical and materials space. However, many properties that ultimately determine the functionality of molecules and materials are not scalar invariants, but response …


























