It is interesting to quantify how novel our newly generated molecules are. We compare these molecules with those that Griffiths et al. collected. We quantify the similarity by computing the distance between molecular fingerprints. Figure visualizes this by laying out the resulting approximate nearest-neighbor graph in two dimensions. The orange and green spheres represent molecules from the Griffiths dataset, the blue spheres show the novel ones, and the pink ones are not even part of the PubChem database. As expected, we find many new structures Fig. | Photoswitch inverse design metrics as a function of temperature. The fraction of valid SMILES indicates the fraction of generated SMILES that can successfully be parsed using RDKit (note that it does not plateau at 0, but approx. 0.1). We then determine the fraction (frac.) of those already part of the training set and find that at low temperature GPT-3 tends to restate molecules from the training set. To quantitatively capture the similarity of the distribution of the generated molecules to the ones from the training set, we compute the Fréchet ChemNet Distance, which quantifies both diversity and distribution match and goes through a minimum at intermediate temperatures. For quantifying how well the generated molecules match the desired transition wavelengths, we use the GPR models reported by Griffiths et al. to predict the transition wavelengths. The dashed horizontal lines in the figure indicate those models' mean absolute error (MAE). Across all temperatures, we found high average synthesizability (SA score 50 smaller 3). The TMAP algorithm builds a nearest-neighbor graph, which is then embedded in two dimensions. Therefore, similar molecules are connected with an edge. We color the points depending on whether they are part of the original dataset of Griffiths et al. but not generated (green), part of the database, and generated by our model (orange). Our models can also generate molecules that have not been part of the photoswitch dataset (note that the model was only trained on 92 molecules from this database). In some cases, those molecules have been reported before and are part of the PubChem database (blue) or are not even part of PubChem (pink). From this figure, we see that the generated molecules sometimes substitutions for molecules in the dataset. In other cases, newly generated molecules introduce a completely new scaffold. For this visualization, we used the TMAP 51 algorithm on photoswitch molecules described using MinHash fingerprint with 2048 permutations. that are derivatives of molecules in the Griffiths database. However, we also find branches that are not part of the library of Griffiths et al. , indicating that the model generated novel kinds of compounds.