> For the complete documentation index, see [llms.txt](https://docs.materialsproject.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.materialsproject.org/methodology/molecules-methodology/legacy-data.md).

# Legacy Data

The first molecular properties presented on the Materials Project were calculated as part of the Electrolyte Genome Project \[1,2], an effort through the Joint Center for Energy Storage Research\[3] to accelerate the design of next-generation battery electrolytes. By design, the Electrolyte Genome aimed to predict only the electrochemical and redox properties of molecules calculated using the adiabatic approximation (see [Redox and Electrochemical Properties](/methodology/molecules-methodology/redox-and-electrochemical-properties.md)). The properties of small molecules were calculated using the B3LYP exchange-correlation functional \[4] and the 6-31+G(d) basis set \[5-11] with a PCM implicit solvent model \[12, 13]. For molecules with more than 50 atoms, the geometries were optimized using the PBE functional \[14] with Grimme's empirical D3 correction \[15].

## References:

1. Qu, X., Jain, A., Rajput, N.N., Cheng, L., Zhang, Y., Ong, S.P., Brafman, M., Maginn, E., Curtiss, L.A. and Persson, K.A., 2015. The Electrolyte Genome project: A big data approach in battery materials discovery. *Computational Materials Science*, *103*, pp.56-67.
2. Cheng, L., Assary, R.S., Qu, X., Jain, A., Ong, S.P., Rajput, N.N., Persson, K. and Curtiss, L.A., 2015. Accelerating electrolyte discovery for energy storage with high-throughput screening. *The journal of physical chemistry letters*, *6*(2), pp.283-291.
3. Trahey, L., Brushett, F.R., Balsara, N.P., Ceder, G., Cheng, L., Chiang, Y.M., Hahn, N.T., Ingram, B.J., Minteer, S.D., Moore, J.S. and Mueller, K.T., 2020. Energy storage emerging: A perspective from the Joint Center for Energy Storage Research. *Proceedings of the National Academy of Sciences*, *117*(23), pp.12550-12557.
4. Becke, A.D., 1993. A new mixing of Hartree–Fock and local density‐functional theories. *The Journal of chemical physics*, *98*(2), pp.1372-1377.
5. Rassolov, V.A., Ratner, M.A., Pople, J.A., Redfern, P.C. and Curtiss, L.A., 2001. 6‐31G\* basis set for third‐row atoms. *Journal of Computational Chemistry*, *22*(9), pp.976-984.
6. Hehre, W\.J., Ditchfield, R. and Pople, J.A., 1972. Self—consistent molecular orbital methods. XII. Further extensions of Gaussian—type basis sets for use in molecular orbital studies of organic molecules. *The Journal of Chemical Physics*, *56*(5), pp.2257-2261.
7. Hariharan, P.C. and Pople, J.A., 1973. The influence of polarization functions on molecular orbital hydrogenation energies. *Theoretica chimica acta*, *28*, pp.213-222.
8. Gordon, M.S., Binkley, J.S., Pople, J.A., Pietro, W\.J. and Hehre, W\.J., 1982. Self-consistent molecular-orbital methods. 22. Small split-valence basis sets for second-row elements. *Journal of the American Chemical Society*, *104*(10), pp.2797-2803.
9. Francl, M.M., Pietro, W\.J., Hehre, W\.J., Binkley, J.S., Gordon, M.S., DeFrees, D.J. and Pople, J.A., 1982. Self‐consistent molecular orbital methods. XXIII. A polarization‐type basis set for second‐row elements. *The Journal of Chemical Physics*, *77*(7), pp.3654-3665.
10. Ditchfield, R.H.W\.J., Hehre, W\.J. and Pople, J.A., 1971. Self‐consistent molecular‐orbital methods. IX. An extended Gaussian‐type basis for molecular‐orbital studies of organic molecules. *The Journal of Chemical Physics*, *54*(2), pp.724-728.
11. Dill, J.D. and Pople, J.A., 1975. Self‐consistent molecular orbital methods. XV. Extended Gaussian‐type basis sets for lithium, beryllium, and boron. *The Journal of Chemical Physics*, *62*(7), pp.2921-2923.
12. Miertuš, S., Scrocco, E. and Tomasi, J., 1981. Electrostatic interaction of a solute with a continuum. A direct utilizaion of AB initio molecular potentials for the prevision of solvent effects. *Chemical Physics*, *55*(1), pp.117-129.
13. Mennucci, B., 2012. Polarizable continuum model. *Wiley Interdisciplinary Reviews: Computational Molecular Science*, *2*(3), pp.386-404.
14. Perdew, J.P., Burke, K. and Ernzerhof, M., 1996. Generalized gradient approximation made simple. *Physical review letters*, *77*(18), p.3865.
15. Grimme, S., Ehrlich, S. and Goerigk, L., 2011. Effect of the damping function in dispersion corrected density functional theory. *Journal of computational chemistry*, *32*(7), pp.1456-1465.