Extract chemical formulas, stability measure, identifier from all NOMAD entries excluding certain periodic elements

NOMAD and FAIRmat
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@mscheidgen @laurih @acarnevali For the stability measures, one option might be to train an ML model on a large database of formation energies, predict the formation energies for NOMAD materials, and use a phase diagram tool to calculate the decomposition energy (or similarly, energy above hull) based on the predicted formation energies, similar to what’s described in DOI: 10.1002/adma.202005112. Then use e_above_hull as a filtering criterion and/or to classify stable vs. non-stable in a “likelihood of stability” sense. At this point, the list of candidates may be sufficiently reduced to “high likelihood of synthesizability” compounds suitable for e.g. arc-melting.

Some literature and tools related to stability and synthesizability:

  1. Wen, C.; Zhang, Y.; Wang, C.; Xue, D.; Bai, Y.; Antonov, S.; Dai, L.; Lookman, T.; Su, Y. Machine Learning Assisted Design of High Entropy Alloys with Desired Property. Acta Materialia 2019, 170, 109–117. Redirecting.

  2. Zhang, Z.; Mansouri Tehrani, A.; Oliynyk, A. O.; Day, B.; Brgoch, J. Finding the Next Superhard Material through Ensemble Learning. Adv. Mater. 2021, 33 (5), 2005112. https://doi.org/10.1002/adma.202005112. (referenced above)

  3. Falkowski, A. R.; Kauwe, S. K.; Sparks, T. D. Optimizing Fractional Compositions to Achieve Extraordinary Properties. Integrating Materials and Manufacturing Innovation 2021. https://doi.org/10.1007/s40192-021-00242-3.

  4. Szczypiński, F. T.; Bennett, S.; Jelfs, K. E. Can We Predict Materials That Can Be Synthesised? Chem Sci 12 (3), 830–840. Can we predict materials that can be synthesised? - Chemical Science (RSC Publishing).

  5. Therrien, F.; Jones, E. B.; Stevanović, V. Metastable Materials Discovery in the Age of Large-Scale Computation. Applied Physics Reviews 2021, 8 (3), 031310. Cookie Absent.

  6. Wang, H.-C.; Botti, S.; Marques, M. A. L. Predicting Stable Crystalline Compounds Using Chemical Similarity. npj Comput Mater 2021, 7 (1), 1–9. Predicting stable crystalline compounds using chemical similarity | npj Computational Materials.

  7. Agrawal, A.; Meredig, B.; Wolverton, C.; Choudhary, A. A Formation Energy Predictor for Crystalline Materials Using Ensemble Data Mining. In 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW); 2016; pp 1276–1279. A Formation Energy Predictor for Crystalline Materials Using Ensemble Data Mining | IEEE Conference Publication | IEEE Xplore.

  8. Bartel, C. J.; Trewartha, A.; Wang, Q.; Dunn, A.; Jain, A.; Ceder, G. A Critical Examination of Compound Stability Predictions from Machine-Learned Formation Energies. npj Comput Mater 2020, 6 (1), 97. A critical examination of compound stability predictions from machine-learned formation energies | npj Computational Materials.

  1. Jang, J.; Gu, G. H.; Noh, J.; Kim, J.; Jung, Y. Structure-Based Synthesizability Prediction of Crystals Using Partially Supervised Learning. J. Am. Chem. Soc. 2020, 142 (44), 18836–18843. https://doi.org/10.1021/jacs.0c07384.
  1. (EDIT) Aykol, M.; Montoya, J. H.; Hummelshøj, J. Rational Solid-State Synthesis Routes for Inorganic Materials. J. Am. Chem. Soc. 2021, 143 (24), 9244–9259. https://doi.org/10.1021/jacs.1c04888.

https://piro.matr.io/ (slick web-app version, currently requires registration and approval)

  1. (EDIT) Peterson, G. G. C.; Brgoch, J. Materials Discovery through Machine Learning Formation Energy. J. Phys. Energy 2021, 3 (2), 022002. ShieldSquare Captcha.

https://www.matlearn.org/

  1. (EDIT) xref: Literature references associated with chemical formulas (or compounds) (mostly in the order in which it was suggested)

And some related tools:

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