Application of materials informatics to vapor-grown carbon nanofiber/vinyl ester nanocomposites through self-organizing maps and clustering techniques

[Display omitted] •Data mining was employed to acquire new information on a new VGCNF/VE framework.•Self-organizing maps, PCA, and FCM clustering techniques were applied.•Different features were ordered in terms of their effects on the responses.•Optimal responses’ values using certain combination(s) of inputs were determined.•The viscoelastic responses of the VGCNF/VE specimens are the most significant. Data mining and knowledge discovery techniques were employed herein to acquire new information on the viscoelastic, flexural, compressive, and tensile properties of vapor-grown carbon nanofiber (VGCNF)/vinyl ester (VE) nanocomposites. Formulation and processing factors (curing environment, presence or absence of dispersing agent, mixing method, VGCNF weight fraction, VGCNF type, high-shear mixing time, and sonication time) and testing temperature were utilized as inputs and the true ultimate strength, true yield strength, engineering elastic modulus, engineering ultimate strength, flexural modulus, flexural strength, storage modulus, loss modulus, and tan delta were selected as outputs. The data mining and knowledge discovery algorithms used in this study include self-organizing maps (SOMs) and clustering techniques. SOMs demonstrated that temperature and tan delta had the most significant effects on the output responses followed by the VGCNF high-shear mixing time, and sonication time. SOMs were also used to produce optimal responses using certain combination(s) of inputs. Fuzzy C-means algorithm (FCM) was also applied to discover patterns in the nanocomposite behavior subsequent to a principal component analysis (PCA), which is a dimensionality reduction technique. Utilizing these techniques, the nanocomposite specimens were separated into different clusters based on the testing temperature (30 °C and 120 °C being the most dominant responses), tan delta, high-shear mixing time, and sonication time. Furthermore, the VGCNF/VE specimens were separated into a cluster based on their viscoelastic responses (storage and loss moduli) at the same temperature. The FCM results indicate that, while all nanocomposite properties in the new framework are essential, the viscoelastic responses of the VGCNF/VE specimens are the most significant. This work highlights the utility of data mining and knowledge discovery techniques in the context of materials informatics for the discovery of patterns and trends in the material behavior that are not immediately known.