Y-Fe-B

[cwf7144]

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 @cwf7144! 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.

[cwf7144]

Y-Fe-B (2).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/93b58e86. For results, please see the table below:

pySIPFENN:	name	SIPFENN_NN30-OQMD [eV/atom]
Y2Fe14B.POSCAR	-0.0774
Y3FeB7.POSCAR	0.1827
Y5Fe2B6.POSCAR	0.2885
YFe2B2.POSCAR	0.3567
YFe4B4.POSCAR	-0.4128
YFeB4.POSCAR	-0.6311

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]
Y2Fe14B.POSCAR	-0.0765	-0.0093	33.8686	138.7862	388.8384	-0.1065	0.0023
Y3FeB7.POSCAR	0.5488	0.0115	59.5372	83.8282	90.2057	0.5076	0.0626
Y5Fe2B6.POSCAR	0.6176	0.0151	55.6916	66.9949	194.966	0.0441	0.0183
YFe2B2.POSCAR	0.2469	-0.0061	47.018	94.8629	164.788	0.076	0.0049
YFe4B4.POSCAR	-0.5113	-0.0339	98.2523	189.7609	150.9346	-0.4951	0.0021
YFeB4.POSCAR	-0.6369	0.1306	171.5543	209.9274	80.1751	-0.5498	-0.0122

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]
Y2Fe14B.POSCAR	-8.1995	-8.2
Y3FeB7.POSCAR	-5.5448	-6.075
Y5Fe2B6.POSCAR	-5.3717	-5.6999
YFe2B2.POSCAR	-7.1529	-7.3328
YFe4B4.POSCAR	-7.9125	-7.9158
YFeB4.POSCAR	-7.7017	-7.7523

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!