Research on machine learning and interatomic potentials is being explored, with a focus on transfer learning and molecular interactions. Some studies involve the use of fast and fourier features, as well as active learning methods, in atomistic and ab initio molecular dynamics simulations. The development of interatomic potentials using machine learning is also being investigated in the context of computational materials.