Is this enough information to fully define a neural network? Background:

A recent paper describes using machine learning to predict the outcome of chemical reactions. In the supplementary information (see pages S77-S78), the neural network used is described. 

I would like to try implementing this network as a first project for learning neural networks and TensorFlow. However, I don't know enough to tell whether the description in the supplementary information is complete.

Question: 

Does the supplementary information (see pages S77-S78 only) provide enough information to implement the network? Or did the author leave a lot out (perhaps on purpose)?

Description of the data for non-chemists: 

The paper considers one specific type of chemical reaction. For this reaction, you have to combine five types of species: molecule 1, molecule 2, a catalyst, a base, and a solvent to dissolve everything. For each species, there are multiple options. You could use a water-based or an alcohol-based solvent, for example. 

There are thousands of possible combinations for this chemical reaction. We have data that pairs each combination with the reaction yield, a number between 0 and 1. We have a bunch of vectors, basically, and we want to predict the yields of new combinations.

Reference:

The recent paper.
Yes.

The neural network mentioned in this paper is simple and straightforward: it is a fully connected network with 2 hidden layers. 
 The authors specified the network structure, the number of neurons in each layer, and the activation functions in each layer and the final loss function.



Given those information, the network is fully specified. For instance, you can create such a network using 

sklearn.neural_network.MLPRegressor(hidden_layer_sizes=(50,7), activation=‘logistic’)