  Precise physical descriptions of molecules can be obtained by solving the
Schrodinger equation; however, these calculations are intractable and even
approximations can be cumbersome. Force fields, which estimate interatomic
potentials based on empirical data, are also time-consuming. This paper
proposes a new methodology for modeling a set of physical parameters by taking
advantage of the restricted Boltzmann machine's fast learning capacity and
representational power. By training the machine on ab initio data, we can
predict new data in the distribution of molecular configurations matching the
ab initio distribution. In this paper we introduce a new RBM based on the Tanh
activation function, and conduct a comparison of RBMs with different activation
functions, including sigmoid, Gaussian, and (Leaky) ReLU. Finally we
demonstrate the ability of Gaussian RBMs to model small molecules such as water
and ethane.
