  In his 2011 work, Maas has shown that the law of any time-reversible
continuous-time Markov chain with finite state space evolves like a gradient
flow of the relative entropy with respect to its stationary distribution. In
this work we show the converse to the above by showing that if the relative law
of a Markov chain with finite state space evolves like a gradient flow of the
relative entropy functional, it must be time-reversible. When we allow general
functionals in place of the relative entropy, we show that the law of a Markov
chain evolves as gradient flow if and only if the generator of the Markov chain
is real diagonalisable. Finally, we discuss what aspects of the functional are
uniquely determined by the Markov chain.
