Grid-less Probabilities with Density Peaks Advanced Density-based clustering techniques form a family of unsupervised machine learning algorithms that group data points within spatial datasets into clusters based on the distance between data points within the data space. Algorithms in this family include DBSCAN and Fast Search and Find of Density Peaks (FSFDP) . There is precedent for the use of FSFDP in molecular conformation spaces, Marinova et al. used it to study the conformation space of Sildenafil 8 . Here, Density Peaks Advanced (DPA) , a successor to FSFDP, is used. DPA, developed by d'Errico et al., splits a set of data points distributed in space into clusters by grouping points within density peaks, a term referring to regions of high data density (N.B. In this work, the term 'density' refers to the density of data points in S, unless otherwise noted). It does this partly by calculating the local density of every region centered on every single point in the dataset. This calculation is a function of the Euclidean distances between the point and its nearest neighbors. Here, the process is applied to a sample of N configurations in conformation space sampled by the MD simulation. These local density calculations are extremely powerful in this context for two key reasons: firstly, each additional dimension in S adds a single term to the Euclidean distance calculation, so the cost with increasing dimensionality scales linearly, and secondly these local densities map a distribution in much the same way as the previously described histogram, so the same reweighing and inversion procedure may be applied to calculate the free energy. These free energies, unlike in the above histogram, are not associated with a defined region of conformation space. Rather, they are associated with a specific configuration sampled by the simulation. Thus, this per point FES has no fixed spatial resolution; data is rich in regions that have been heavily sampled and sparse in regions that have not been frequented. This is advantageous as it means that while data in the local minima remains highly dense due to the frequent sampling, little cost is incurred considering data from the rarely visited, largely irrelevant high-energy regions. This contrasts with the grid-based approach, where these high-energy regions are modeled in as high a resolution as the more relevant local minima. The results of applying this approach to alanine dipeptide can be seen in Figure .