Pierre-Paul De Breuck

Hi @sgbaird ! Great work and thank you for taking the lead on this! I like how you suggest presenting the information, however I'm not convinced on using the interval...

Thanks @naik-aakash for spotting this ! I'll merge this, sorry for the latency !

Issue still present - can now be elegantly fixed by using (or adapting) our Imputer to remember and impute missing features needed for prediction. Sidenotes: - It would be better...

Tagging @gbrunin as partially involved in this.

Hi @sgbaird, There is at this stage nothing that "easily" includes state variables (at least not expicitely). Though, two quick solutions exists. If the properties are available on a fixed...

For solution (1), yes the idea would be to have one target per temperature, like the thermodynamical data [notebook](https://github.com/ppdebreuck/modnet/blob/master/example_notebooks/predicting_vib_thermo.ipynb). ```python # Creating MODNetModel model = MODNetModel([[["S_5K","S_300K","S_500K"]]], {"S_5K":1,"S_300K":1,"S_500K":1}, num_neurons=[[256],[64],[64],[32]], ) ```...

Cool! Indeed, option (1) was infeasible here. Thanks for this addition. A simple hyper opt might be worth adding as example: ```python from modnet.hyper_opt import FitGenetic ga = FitGenetic(train) model...

`FitGenetic.run()` will in fact always return an EnsembleModel, with the ensemble depending on the `refit` and `nested` argument. - If refit = 0: No refitting is done. Fitted models from...

Hi! Thanks for pulling this up. This hasn't been implemented but can easily be added manually. Small example: ```python from modnet.featurizers.presets import DeBreuck2020Featurizer from modnet.preprocessing import MODData from pymatgen.core import...

**The Bayesian module is somewhat deprecated**, as the results didn't turn out as good as hoped and the **EnsembleMODNetModel should be preferred**. Nevertheless, sampling can be done using model.predict(x) (single...