ML data for crystals for Ce-Rh binary system

amkrajewski / MPDD-X

[playing around for now] One stop solution for getting material properties with ML models while contributing to a community database.

MIT License

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ML data for crystals for Ce-Rh binary system #36

Open hamzabouchta opened 2 months ago

hamzabouchta commented 2 months ago

You do not need to put in anything here :)

Just open the issue by clicing the green "Submit new issue" and within 10-15s a message instructing you do upload your files should appear!

github-actions[bot] commented 2 months ago

Hello @hamzabouchta! Please comment below on this issue while attaching a ZIP file with atomic structures (POSCAR/CIFs) to get started! You can put keywords describing it in the body of the comment.

As soon as you do it, your atomic structures will be processed through several tools, described below, and return a neat Markdown report.

pySIPFENN framework, returning (1) array of descriptors (feature vectors) in Numpy .npy and CSV formats you can use for your ML modelling, alongside formation energy predictions.
ALIGNN framework, returning (1) results from 7 ALIGNN models specified here and (2) compressed graph representation files.
CHGNet model, returning (1) energy prediction for your input, (2) CHGNet-relaxed structures in the same format (POSCAR/CIF) as your input, and (3) energy prediction for the relaxed structures.

hamzabouchta commented 2 months ago

Ce-Rh_relaxed_structures POSCAR.zip

github-actions[bot] commented 2 months ago

👍 [1/3] I found the ZIP file linked in your comment. I will now attempt to download it and validate contents!

github-actions[bot] commented 2 months ago

👍 [2/3] I received your files and extracted them! Now, I will set up the calculation environment and run: pySIPFENN, CHGNet, and ALIGNN. You can now have A coffee and wait for results. Obtaining results should take at least a couple of minutes.

github-actions[bot] commented 2 months ago

🚀 [3/3] All Done! Your submission and all results been persisted under contrib.mpdd.org/3760a0c7. For results, please see the table below:

pySIPFENN:	name	SIPFENN_NN30-OQMD [eV/atom]
Ce3Rh2.POSCAR	-0.5413
Ce7Rh3.POSCAR	-0.4546
Ce.POSCAR	0.0123
CeRh2.POSCAR	-0.4351
CeRh3.POSCAR	-0.2374
CeRh.POSCAR	-0.6209
Rh.POSCAR	0.0127

ALIGNN:	name	ALIGNN-JARVIS Formation Energy [eV/atom]	ALIGNN-JARVIS MBJ Bandgap [eV]	ALIGNN-JARVIS Shear Modulus [GPa]	ALIGNN-JARVIS Bulk Modulus [GPa]	ALIGNN-JARVIS Exfoliation Energy [meV/atom]	ALIGNN-MP Formation Energy [eV/atom]
Ce3Rh2.POSCAR	-0.682	-0.0327	20.9771	78.6622	437.7096	-0.6355	0.0124
Ce7Rh3.POSCAR	-0.5674	-0.0521	22.6173	60.5609	421.9771	-0.4782	0.0064
Ce.POSCAR	-0.0031	0.0164	25.2531	36.7132	397.634	0.0896	0.0139
CeRh2.POSCAR	-0.7471	-0.003	52.0563	155.1705	456.3898	-0.7172	0.0064
CeRh3.POSCAR	-0.6441	-0.004	67.2306	164.0305	428.9031	-0.6446	0.0022
CeRh.POSCAR	-0.8061	-0.0621	28.4887	101.2792	412.9973	-0.7578	0.0086
Rh.POSCAR	-0.0076	-0.0087	155.753	257.249	376.4872	-0.0021	0.0102

CHGNet:

Please note that these are energies, not formation energies.	Name	CHGNet_0.3.0-MP Energy [eV/atom]
Ce3Rh2.POSCAR	-7.1149	-7.1149
Ce7Rh3.POSCAR	-6.7931	-6.7931
Ce.POSCAR	-5.8037	-5.8609
CeRh2.POSCAR	-7.5996	-7.5996
CeRh3.POSCAR	-7.6745	-7.6745
CeRh.POSCAR	-7.3926	-7.3926
Rh.POSCAR	-7.2977	-7.3003

For the next 30 days, you can also download other results like pySIPFENN's raw descriptor data Numpy array and CHGNet-relaxed structure files under Artifact tab here Enjoy!