  Traditional force-fields cannot model chemical reactivity, and suffer from
low generality without re-fitting. Neural network potentials promise to address
these problems, offering energies and forces with near ab-initio accuracy at
low cost. However a data-driven approach is naturally inefficient for
long-range interatomic forces that have simple physical formulas. In this
manuscript we construct a hybrid model chemistry consisting of a nearsighted
Neural-Network potential with screened long-range electrostatic and
Van-Der-Waals physics. This trained potential, simply dubbed "TensorMol-0.1",
is offered in an open-source python package capable of many of the simulation
types commonly used to study chemistry: Geometry optimizations, harmonic
spectra, and open or periodic molecular dynamics, Monte Carlo, and nudged
elastic band calculations. We describe the robustness and speed of the package,
demonstrating millihartree accuracy and scalability to tens-of-thousands of
atoms on ordinary laptops. We demonstrate the performance of the model by
reproducing vibrational spectra, and simulating molecular dynamics of a
protein. Our comparisons with electronic structure theory and experiment
demonstrate that neural network molecular dynamics is poised to become an
important tool for molecular simulation, lowering the resource barrier to
simulate chemistry.
