Formation Energy Predictor

Disclaimer: The results from this tool are estimates based on data-driven analytics on simulation data. All results are provided for informational purposes only, in furtherance of the developers' educational mission, to complement the knowledge of materials scientists and engineers, and assist them in their search for new materials with desired properties. The developers may not be held responsible for any decisions based on this tool.

Welcome to the online formation energy predictor. This tool deploys data mining models to predict the formation energy (a measure of stability; more negative values implying more stable) of a material based on its chemical composition. The predictive models deployed here have been built on hundreds of thousands of Density Functional Theory (DFT) calculations on crystalline materials from the Open Quantum Mechanical Database (OQMD), and run many orders of magnitude faster than DFT.

In order to use this tool, please provide the list of chemical compositions in the text box below, and click Submit. Please ensure that each chemical formula respects the charge balance condition with common oxidation states of individual elements. The elements greyed out in the periodic table below may not be used. The predictions from different models are presented in a table (sortable by clicking the header).

Chemical composition(s)

separated by white spaces, e.g. "NaCl H2SO4 Na2CO3 CaN2O6 Cs2Te2Pt SiYb3F5 Pm2S3"

Please select the models you wish to run:

 [PRB'14]    [ICDM'16]    [ElemNet] New!

Developer team: Ankit Agrawal, Dipendra Jha, Arindam Paul, Wei-keng Liao, Alok Choudhary

Collaborators: Chris Wolverton, Logan Ward, Bryce Meredig


This work was performed under the following financial assistance award 70NANB14H012 from U.S. Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design (CHiMaD). The authors also acknowledge partial support from AFOSR award FA9550-12-1-0458.


D. Jha, L. Ward, A. Paul, W-k. Liao, A. Choudhary, C. Wolverton, and A. Agrawal, "ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition," Scientific Reports, Volume 8, Article number: 17593, 2018. [url]

A. Agrawal, B. Meredig, C. Wolverton, and A. Choudhary, "A Formation Energy Predictor for Crystalline Materials Using Ensemble Data Mining," in Proceedings of IEEE International Conference on Data Mining (ICDM) (Demo), 2016, pp. 1276–1279. [url]

B. Meredig, A. Agrawal, S. Kirklin, J. E. Saal, J. W. Doak, A. Thompson, K. Zhang, A. Choudhary, and C. Wolverton, “Combinatorial screening for new materials in unconstrained composition space with machine learning,” Physical Review B (PRB), vol. 89, no. 094104, pp. 1–7, 2014. [url]

Center for Ultra-scale Computing and Information Security (CUCIS), EECS Department, Northwestern University, Evanston, IL 60208, USA