formation enthalpie of the Ce-Rh binary system

[hamzabouchta]

We are happy to quickly add any structure-informed model which is a plug-and-play addition to our infrastructure, which will generally mean it either:

• Utilizes already implemented representation, feature set, or its subset. Please check below as appropriate:

  • [ ] My model uses KS2022 or its subset.
  • [ ] My model uses ALIGNN graph representation.
  • [ ] My model is a refitting or a variation of CHGNet
  • [ ] My model is available from a public citable source like Zenodo (preferred) or figshare.
  • [ ] My model can be fetched within 30s, corresponding to ~2GB on Zenodo or 250MB on figshare.
  • [ ] My model can run single-threaded in under 500ms / structure
  • [ ] My model can run single-threaded in under 5s / structure

• Is a lightweight tool, which can be quickly installed and run. Please check below as appropriate:

  • [ ] My model is well maintained and has been used in past research.
  • [ ] My model is actively tested at least weekly and has been well-maintained for at least 6 months.
  • [ ] My model is available from a public citable source like Zenodo (preferred) or figshare.
  • [ ] My model can be fetched within 30s, corresponding to ~2GB on Zenodo or 250MB on figshare.
  • [ ] My model can run single-threaded in under 500ms / structure
  • [ ] My model can run single-threaded in under 5s / structure

If it does not fall into the above categories, please dont get discouraged and still let us know! It will be much more work on our side, but we will probably be happy to work with you. To get started on that, please explain it in a few sentences.

[github-actions[bot]]

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]

Ce-Rh_relaxed_cif.zip

[github-actions[bot]]

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

[github-actions[bot]]

👍 [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.

[amkrajewski]

@hamzabouchta The CIF files you provided were incompatible with pymatgen for some reason. Here are POSCAR files corresponding the the CONTCARs you've uploaded earlier. Ce-Rh_relaxed_structures.zip

[github-actions[bot]]

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

[github-actions[bot]]

👍 [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.

[amkrajewski]

Ce-Rh_relaxed_structures.zip

[github-actions[bot]]

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

[github-actions[bot]]

👍 [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]]

🚀 [3/3] All Done! Your submission and all results been persisted under contrib.mpdd.org/11461664. 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]	ALIGNN-MP PBE Bandgap [eV]
Ce3Rh2.POSCAR	-0.6896	-0.0304	21.1214	78.9481	438.9398	-0.6388	0.0116
Ce7Rh3.POSCAR	-0.5686	-0.0541	22.6941	61.0827	421.6457	-0.4776	0.0064
Ce.POSCAR	-0.002	0.0199	25.6281	36.7025	398.2325	0.0869	0.0143
CeRh2.POSCAR	-0.7464	-0.0023	51.9232	154.9653	454.6729	-0.7146	0.0063
CeRh3.POSCAR	-0.6446	-0.0039	66.7432	163.6737	428.2826	-0.6458	0.0044
CeRh.POSCAR	-0.8068	-0.0487	29.6937	101.3445	424.5614	-0.7628	0.0004
Rh.POSCAR	-0.0055	-0.0061	155.7439	257.8409	375.0273	-0.0012	0.0089

CHGNet:

Please note that these are energies, not formation energies.	Name	CHGNet_0.3.0-MP Energy [eV/atom]	CHGNet_0.3.0-MP Relaxed 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!