enthalpie of formation of the Eu-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]

Eu-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.

[hamzabouchta]

Eu-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 processing is failing because CIFs are not properly formatted (according to both pymatgen and ASE packages). Same as in the previous case. Try using POSCAR files like I did here https://github.com/amkrajewski/MPDD-X/issues/32

[hamzabouchta]

@amkrajewski Hello Sir, thank you for your message. Unfortunately, I don't have any POSCAR files. Would it be possible for you to provide them to me or to indicate where I could find them? I'm willing to try this approach and see if it resolves the issue we're encountering. Thank you in advance for your assistance."

[amkrajewski]

@hamzabouchta I converted them for you using CrystalMaker software. I think the issue had to do with having two structures in every CIF file. Eu_Rh_relaxed_vasp.zip

[amkrajewski]

Eu_Rh_relaxed_POSCAR.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/d0451934. For results, please see the table below:

pySIPFENN:	name	SIPFENN_NN30-OQMD [eV/atom]
Eu2Rh.POSCAR	-0.4749
Eu3Rh.POSCAR	-0.4481
Eu5Rh2.POSCAR	-0.4841
Eu-cub.POSCAR	0.0136
EuRh2.POSCAR	-0.5485
Eu_Hexag.POSCAR	0.0284
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]
Eu2Rh.POSCAR	0.4257	-0.0216	27.0466	95.1086	489.131	0.0887	0.0106
Eu3Rh.POSCAR	-0.4881	0.0041	22.6663	48.939	481.842	-0.3199	0.0116
Eu5Rh2.POSCAR	-0.4869	-0.0045	22.9491	54.3822	477.0386	-0.3186	0.0067
Eu-cub.POSCAR	0.0559	-0.001	11.8909	35.1605	527.8611	0.0993	0.0214
EuRh2.POSCAR	-0.7271	-0.0228	60.3726	159.6115	484.2272	-0.4711	0.0117
Eu_Hexag.POSCAR	0.0012	0.0082	14.9345	34.3951	525.8373	0.0383	0.0155
Rh.POSCAR	-0.0055	-0.0061	155.7247	257.824	375.0375	-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]
Eu2Rh.POSCAR	-8.6259	-8.7071
Eu3Rh.POSCAR	-9.8565	-9.8565
Eu5Rh2.POSCAR	-9.7606	-9.7853
Eu-cub.POSCAR	-10.1892	-10.2562
EuRh2.POSCAR	-8.7706	-8.7706
Eu_Hexag.POSCAR	-10.2208	-10.2513
Rh.POSCAR	-7.2976	-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!