In the fields of chemistry and materials, most successful, widely used machine learning schemes introduced over the last decade aim to model molecular energies or interatomic potentials. Accordingly, the representations used to map atomic configurations into vectors of descriptors or features used as model inputs reflect fundamental properties of the interatomic potential such as invariance to permutation between identical atoms, rigid rotation or inversion of the molecular structure. They also reflect notions of locality and nearsightedness—the idea that potential local electronic properties depend significantly on the effective external potential only at nearby points—of many components of interatomic energy.
Click here for original story, Equivariant representations for molecular Hamiltonians and N-center atomic-scale properties
Source: Phys.org