  We introduce an extension to the PySCF package which makes it automatically
differentiable. The implementation strategy is discussed, and example
applications are presented to demonstrate the automatic differentiation
framework for quantum chemistry methodology development. These include orbital
optimization, properties, excited-state energies, and derivative couplings, at
the mean-field level and beyond, in both molecules and solids. We also discuss
some current limitations and directions for future work.
