Every DFT re-optimization gives us information about the conformation space of a molecule. Do we have enough information to decide we have gone far enough along the priority list and it is time to stop re-optimizations? To investigate this, we used our dataset of exhaustive DFT re-optimizations on FF conformation searches of 150 molecules (a total 15342 FF conformers) to build a machine learning model for answering the question. As we re-optimize FF structures of a molecule following a priority list, we can calculate the mole amount relative to the current global minimum of the new conformer, χnew, for each new re-optimized structure based on all conformers re-optimized so far (eq. 2; ΔGnew is the ΔG of the new conformer compared to the global minimum in the re-optimized structure so far). χnew = exp(-ΔGnew/RT) (2) If the new structure is a duplicate of an earlier one, we set χnew to zero. We also know the proportion of the FF conformers that have been re-optimized which we call ropt. After exhaustive DFT re-optimizations of every conformer, we can plot the χnew against ropt for the molecule (an example in Figure ). The global minimum structure corresponds to the point with the highest ropt value with χnew = 1 on the complete χnew vs ropt plot. How do we decide when to stop? With an exhaustive χnew vs ropt plot, we can determine PGMT, the value of ropt at which the global minimum is found. Usually, however, we rarely re-optimize every conformer from the conformational searching output and so we do not know PGMT. Partial re-optimizations of the FF structures can provide an incomplete χnew vs ropt plot. We decided to employ a machine learning model to investigate if the data from an incomplete χnew vs ropt plot is sufficient to decide if the re-optimization process structure can be terminated because the global minimum has probably been found. Our DFT dataset can provide the data for the model training. In addition to the full χnew vs ropt plot, each molecule should give m-1 incomplete χnew vs ropt plot, where m is the total number of conformers. The label (ie continue or stop) is assigned to each χnew vs ropt plot by comparing ropt value of the last data point to the corresponding PGMT. Features of a χnew vs ropt plot were extracted as descriptors: