The spontaneous self-assembly of block copolymers (BCPs) to generate patterns and motifs with sub-lithographic resolution has been of great interest for over two decades as an alternative or complementary technique to photolithography. When integrated with sparse guiding morphological or chemical features produced via traditional lithography, these low cost polymer processing methods enable the generation of templates for production of sub-10 nm features with a high degree of long range order. This combination of self-assembly with lithography is termed directed self-assembly (DSA) and has been primarily directed towards applications in microelectronics, including memory storage materials, finFET, and vias. These nanopatterned substrates have also seen use as catalysts for growth of ordered nanowire arrays, as a platform for protein detection, separation membranes, surface enhanced Raman spectroscopy (SERS) substrates, anti-reflective coatings in photovoltaics, and chemical and biomedical sensors. The self-assembly of a monolayer of a given BCP on a flat, featureless surface results in a polycrystalline morphology with uncorrelated nano-to micron-sized domains, with a significant concentration of defects. The ITRS (now IDRS roadmap) has specified a target of a maximum defectivity of ~1 per 100 cm -2 , which is several orders of magnitude lower than that quantified in patterns derived from these monolayers of self-assembled BCPs. Some kinetically trapped non-equilibrium metastable configurations in the free energy landscape persist even through long annealing times, which is an issue for high volume manufacturing on 300 mm wafers. In an attempt to avoid defect formation during the course of self-assembly, much effort has been directed towards the optimization of annealing techniques of the spin-coated BCP films, including solvent vapor, thermal (including microwave irradiation), a combination of solvent and thermal (solvothermal), laser annealing, shear flow, and annealing within an electric field. Lithography multiplication via DSA also reduces defect density as chemical and morphological features on the surface help to guide the BCP into the lowest energy equilibrium patterns. Defects still remain a challenge, however, particularly with smaller molecular weight BCPs with a high Flory-Huggins interaction parameter (𝜒 ! > 10.5) that produce the sub-20 nm periodicities, and features. To achieve the desired nanopattern via self-assembly, not only are the annealing parameters critical, but the initial thickness of the applied BCP monolayer must be precise, typically within one or two nanometers. The ideal thickness for a given BCP is unique, and depends upon the composition of the BCP and factors such as surface functionalization and energy, the use of a topcoat and other factors. Both experimental and computational results strongly link initial film thickness with the resulting self-assembled structure and persistent defects. Small fluctuations of thickness within a monolayer film can lead to different self-assembled structures as unfavorably thin domain thicknesses frustrate packing due to the buildup of strain. Periodicity of structures formed from self-assembled BCP nanopatterns may be dependent upon film thickness and other processing parameters, as has been recently shown with bottlebrush BCPs. The defects observed in self-assembled monolayers of BCPs that form hexagonal dot patterns are dominated by disclinations and dislocations, as well as point defects along grain boundaries between uncorrelated domains, multilayers, and the presence of lamellae. These hexagonal dot-based nanopatterns are of interest for memory materials and devices, applications that are more tolerant of defects than linear patterns for CMOS (e.g. vias), and thus the route towards commercialization appears more direct. 1 "Further improvement of process-friendly techniques" 1 is, however, still required, particularly over large areas. With the optimization of self-assembly of any new BCP, particularly high 𝜒 BCPs that may be challenging due to longlived metastable defects, or any change in processing conditions, annealing parameters will need to be re-evaluated. Empirical optimization of annealing of BCP films is time-consuming and if not rationally devised, will incompletely sample variable space. In an attempt to optimize these SVA parameters more efficiently with fewer laboratory resources (including time, one of the most precious resources), we use a full factorial experimental design complemented by machine learning (ML) approaches to identify the optimized SVA parameters. Prior to optimizing BCP dot arrays for minimum defectivity we first rigorously define what constitutes a defect, with respect to making bit-patterned media devices. Then, we define a figure of merit (FOM) that encompasses defect density and the spatial distribution of these defects at all relevant length scales. However, the factors involved in solvent annealing are non-orthogonal and correlated, with one alteration affecting other parameters of the film during self-assembly, and the effects playing out over different length scales. Given the difficulties of optimizing correlated systems with numerous input parameters, we show how ML techniques may be applied to characterize, quantify, predict, and ultimately minimize the defect densities of solvent-annealed hexagonal dotforming BCP nanopatterns.