Essentiality of the Basis Function in Deep Learning Physical Chemistry Properties

The deep learning (DL) method sometimes suffers from poor transferability due to the wrong structure, i.e., basis functions used as the kernel for dimension elevation. Thus, for solving specific problems, a broad and robust set of basis functions should be elaborated. The choice of basis is similar to the procedure in variational principles in quantum physical chemistry applications. This work applies the associated Laguerre and the spherical harmonic for feature generation of molecules and atomic interactions, showing better performance in both the real-space and momenta-space and significantly enhancing the transferability and interpretability to unfamiliar elements and structures over the previous models. Moreover, with the symmetry manifested by the angular part from the spherical harmonics, both the energy and distribution of excited state orbitals can be calculated. Besides, the special functions can also be used in other applications such as image classification.