The author proposed a new multi-conditional molecules generation model as an improvement of the existing SSVAE, as it could take partially labeled chemistry dataset for training. The new model is then validated in the task of designing molecules for LHCE diluents. This work should be considered as a noticeable improvement of existing methods. However, there are few concerns and minor corrections should be addressed before publication:

1. When comparing the RNN-based and (pre-trained) BERT-based ConGen model, there are two entangled factors: neural network architecture and pre-trained model.

a. For the architecture, attention-based VAE (bert-based ConGen, in this case) is known to perform better than RNN in many ways, except the computational cost. Author does not explain why attention-based architecture is expected to perform worse in this case.
b. The author does a good job in explaining why pre-trained models performed worse in this case. However, the data cannot support this conclusion because RNN-based ConGen vs. pre-trained BERT-based ConGen has significantly different architectures.

Thus I suggest
a. Since the ChemBERTa is pre-trained on molecules that have previously been shown in the literature, its output should have better stability and synthesizability. Taking these dimensions into consideration could make the comparison more complete. For example, consider the Synthetic Accessibility Score or similar metrics.

b. Add the discussion between training-from-scratch BERT-based models to the main text. It is worth mentioning that the training-from-scratch BERT-based model outperformed the RNN-based model in some Conditional Generation tasks, based on the table in supplemental section Impact of ConGen BERT 𝜷 Variations and Training from Scratch.

1. It will make the work more self-consistent if the author could propose a filtering mechanism. Right now, the query generates too many data points without enough stability, synthesizability and novelty. Without a valid filtering method, the author failed to validate those proposed candidates (27838 candidates!) with quantum chemistry methods because of the high computational cost.

2. [Minor correction] Table 4 is a very nice representation of the advantage of the ConGen model. However there are way too many candidates and the author didn’t explain why those are chosen. It would be nice if the author could list the chemical structure of these candidates and only present the most promising ones, instead of all of them. I am confident this will be very helpful for the whole lithium battery community.

3. [Minor correction] In section ‘Baseline SSVAE Model’, please explain how the molecule structure is embedded. I am assuming the model is using SMILES and one-hot encoding but it is not stated in the main text.

4. [Minor correction] In section ‘Baseline SSVAE Model’: ‘where a beam search algorithm is used for converting output 𝒙𝑫 to a molecule SMILES’. Please explain how beam search is used here. Per my understanding, converting $x?klzzwxh:0002?D$ to SMILES is trivial enough that a beam search is not necessary. The original SSVAE model used beam search, but only for converting the probability to $x?klzzwxh:0003?D$ not $x?klzzwxh:0004?D$ to SMILES.

Author has made notable work in terms of addressing the comments. The introduction of new metrics (synthetic accessibility) made this work more comprehensive. The modification of Table.4 is a step forward from the aspect of presentation.