Change the target properties

A different value

generation:
  targets:
    - {band_gap: 1.2}
    - {band_gap: 1.8}      # each entry is its own search

A different kind of goal

loss says how a prediction is compared with the target:

loss	meaning	typical use
l2	squared distance (default)	hit a value
l1	absolute distance	hit a value, less sensitive to outliers
at_most	only values above the target are penalised	formation enthalpy ≤ −0.1 eV/atom
at_least	only values below are penalised	dielectric constant ≥ 20

objectives:
  - {property: band_gap,   loss: l2,       weight: 10000, select_weight: 1.0}
  - {property: enthalpy,   loss: at_most,  weight: 6000,  select_weight: 0.4}

weight acts during the latent search; select_weight when ranking finished candidates.

A different property

The model knows the properties it was trained on (meidnet info model.pt lists them). To steer a property that is not in the model, add it to the data and retrain.

In the Studio

Drag the target slider; the predicted-closest table and the scatter update at once, and a warning appears if the target lies outside the training range. Export the YAML when you are happy.