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Home Page: https://ml4chem.dev
License: Other
ML4Chem: Machine Learning for Chemistry and Materials
Home Page: https://ml4chem.dev
License: Other
Loading in a pre-trained model in PyTorch is simple:
from torchvision import models
model = model.vgg16(pretrained=True)
I think the pretrained
keyword argument is something that has to do with the vgg16
model but should not be complicated to implement.
Data structure is quite important in machine-learning and in fact, it can represent a bottleneck as one has to iterate over the data points.
The Data class in mlchemistry, at least I hope, has to be done in a way that can interoperate with other backends and has to be designed to build data for more than neural networks (I support vector machines).
This function will take as an argument the path of a cjson
file and will return an ASE
trajectory file that is going to be compatible with ml4chem.
For more information see https://github.com/OpenChemistry/chemicaljson
Make the code to create feature vectors correctly using pure python.
The reason for this is because we have the energy of an image and within the atom-centered model we have to guess atomic energies that when summed up they return the target -- in this case the total energy. This same loss function can be then adjusted to train against forces if wanted.
Another one to the zoo.
The NNs are being trained correctly. Now, it is time to add methods to save()
and load()
models.
The biases are not correctly initialized. I should create some kind of trick to have them starting to values that make sense for the problem in question.
I already did this before. I have to add this model and modify the data class so that the code is not slow. This is related to issue #2.
sigma
per atom.That class would help to merge models like this:
Implement model shown in Ghasemi, S. A., Hofstetter, A., Saha, S. & Goedecker, S. Interatomic potentials for ionic systems with density functional accuracy based on charge densities obtained by a neural network. Phys. Rev. B 92, 045131 (2015)
.
Use the Gaussian fingerprints to train a pytorch neural network with energy target vector.
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