Here, I used a big grid search. I believe if we do a fine grid search we can get a lower mae and better r2. I think we couldn't get any nice result for RDF only is because machine learning is dataset-dependent. In their paper, they didn't mention RDF only result.
I will integrate this machine learning method in the ML-Materials repo today so it can be reproduceable.
The GridSearch for Gradientboosting method just got done.
Qiang mentioned to try RDF + Chem to predict enthalpy formation. I got a similar result to what discussed in Jarvis paper (https://journals.aps.org/prmaterials/pdf/10.1103/PhysRevMaterials.2.083801). The result in our simulation is r2 = 0.939 and MAE = 0.156.
Here, I used a big grid search. I believe if we do a fine grid search we can get a lower mae and better r2. I think we couldn't get any nice result for RDF only is because machine learning is dataset-dependent. In their paper, they didn't mention RDF only result.
I will integrate this machine learning method in the ML-Materials repo today so it can be reproduceable.