Do you know of curated experimental Vickers hardness datasets with composition, temperature, and load?

This is in the context of materials discovery for superhard materials, a classic problem in materials informatics :slight_smile: . A lot of approaches use elastic moduli as a screening tool, but this doesn’t seem very amenable to an adaptive design scheme unless DFT gets incorporated into the workflow (i.e. predict, measure experimental hardness, characterize crystal structure, calculate elastic moduli via DFT, repeat).

The options I’ve found so far:

Aside: there are 277 shared (unique) compositions between VickersHardnessPrediction and MPDS (to get this, I removed duplicates within each set, converted the formulas to composition-based feature vectors via CBFV package, scaled the features to (0,1) via MinMaxScaler(), rounded to 2 decimals via np.round(decimals=2), and dropped duplicates via df.drop_duplicates()). In other words, there are 783 unique compositions collectively between the two datasets.

Any alternative datasets or other thoughts?

1 Like

There is the upcoming data update of the properties part on the MPDS, planned for the next month (Pauling File release 2022). So the counts might be increasing soon, but not radically.

1 Like