Legacy Data
Describing the data present in the original Molecule Explorer
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). 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:
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Mennucci, B., 2012. Polarizable continuum model. Wiley Interdisciplinary Reviews: Computational Molecular Science, 2(3), pp.386-404.
Perdew, J.P., Burke, K. and Ernzerhof, M., 1996. Generalized gradient approximation made simple. Physical review letters, 77(18), p.3865.
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.
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