Figure shows loss, , and integer loss, I , after network training on the nonisothermal data set comparing both the standard (eq. ( )) as well as the reparameterized (eq. ( )) Arrhenius expression. The difference in )) and the reparameterized (eq. ( )) Arrhenius expression. loss, , between both parameterizations is striking. With only minor deviations from integer stoichiometric coefficients (compare I ), stoichiometry can be discovered over the whole training range with both methods. With the stoichiometry recovered for all sampled temperature ranges and constant integer losses, I , (compare fig. ), the difference in overall loss can solely be attributed to the (miss)identification of the pre-exponential constants, A, and activation energies, E A , differing between both parameterizations. From these results it is apparent that the reparameterized expression performs better in terms of minimizing error alone. Making use of the physically informed network architecture, the network predicted surface reaction rate constant at T ref , k + S,ref , is calculated for each ∆T (eq. ( )) and depicted in fig. alongside the networkidentified absolute value of the activation energy, E A .