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Machine learning reveals orbital interaction in materials

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Pages 756-765 | Received 30 Jun 2017, Accepted 07 Sep 2017, Published online: 26 Oct 2017

References

  • Yousef S, Da G, Thanh N, et al. Data mining for materials: computational experiments with compounds. Phys Rev B. 2012;85:104104.
  • Yang S, Lach-hab M, Vaisman II, et al. Identifying zeolite frameworks with a machine learning approach. Phys Chem C. 2009;113:21721–21725.
  • Hautier G, Fischer CC, Jain A, et al. Finding nature’s missing ternary oxide compounds using machine learning and density functional theory. Chem Mater. 2010;22:3762–3767.
  • Snyder JC, Rupp M, Hansen K, et al. Finding density functionals with machine learning. Phys Rev Lett. 2012;108:253002.
  • Isayev O, Fourches D, Muratov EN, et al. Materials cartography: representing and mining materials space using structural and electronic fingerprints. Chem Mater. 2015;27:735–743.
  • Ghiringhelli LM, Vybiral J, Levchenko SV, et al. Big data of materials science: critical role of the descriptor. Phys Rev Lett. 2015;114:105503.
  • Dam HC, Pham TL, Ho TB, et al. Data mining for materials design: a computational study of single molecule magnet. J Chem Phys. 2014;140(4):044101.
  • Pham TL, Kino H, Terakura K, et al. Novel mixture model for the representation of potential energy surfaces. J Chem Phys. 2016;145(15):154103.
  • Behler J, Parrinello M. Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys Rev Lett. 2007;98:146401.
  • Behler J. Atom-centered symmetry functions for constructing high-dimensional neural network potentials. J Phys Chem. 2011;134:074106.
  • Artrith N, Kolpak AM. Understanding the composition and activity of electrocatalytic nanoalloys in aqueous solvents: a combination of dft and accurate neural network potentials. Nano Lett. 2014;14(5):2670–2676.
  • Eshet H, Khaliullin RZ, Kuhne TD, et al. Ab initio quality neural-network potential for sodium. Phys Rev B. 2010;81:184107.
  • Eshet H, Khaliullin RZ, Kuhne TD, et al. Microscopic origins of the anomalous melting behavior of sodium under high pressure. Phys Rev Lett. 2012;108:115701.
  • Artrith N, Morawietz T, Behler J. High-dimensional neural-network potentials for multicomponent systems: applications to zinc oxide. Phys Rev B. 2011;83:153101.
  • Artrith N, Behler J. High-dimensional neural network potentials for metal surfaces: a prototype study for copper. Phys Rev B. 2012;85:045439.
  • Bartók AP, Payne MC, Kondor R, et al. Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons. Phys Rev Lett. 2010;104:136403.
  • Bartók AP, Cs\’{a}nyi G. Gaussian approximation potentials: a brief tutorial introduction. Int J Quantum Chem. 2015;115(16):1051–1057.
  • De S, Bartók AP, Csanyi G, et al. Comparing molecules and solids across structural and alchemical space. Phys Chem Chem Phys. 2016;18:13754–13769.
  • Rupp M, Tkatchenko A, Muller KR, et al. Fast and accurate modeling of molecular atomization energies with machine learning. Phys Rev Lett. 2012;108:058301.
  • Faber F, Lindmaa A, von Lilienfeld OA, et al. Crystal structure representations for machine learning models of formation energies. Int J Quantum Chem. 2015;115(16):1094–1101.
  • Matthias R. Machine learning for quantum mechanics in a nutshell. Int J Quantum Chem. 2015;115(16):1058–1073.
  • Seko A, Hayashi H, Nakayama K, et al. Representation of compounds for machine-learning prediction of physical properties. Phys Rev B. 2017;95:144110.
  • Pilania G, Wang C, Jiang X, et al. Accelerating materials property predictions using machine learning. Sci Rep. 2013;3:2810 (1--6).
  • Kotz JC, Treichel P, Townsend J, editors. Chemistry and chemical reactivity. Belmont, CA: Brooks/Cole; 2008.
  • Jean Y, Marsden C, editors. Molecular orbitals of transition metal complexes. New York: Oxford University Press; 2005.
  • O’Keeffe M. A proposed rigorous definition of coordination number. Acta Cryst. 1979;A35:772–775.
  • Ong SP, Richards WD, Jain A, et al. Python materials genomics (pymatgen): a robust, open-source python library for materials analysis. Comput Mater Sci. 2013;68:314–319.
  • Jain A, Ong SP, Hautier G, et al. Commentary: the materials project: a materials genome approach to accelerating materials innovation. APL Mater. 2013;1(1):011002.
  • Ong SP, Cholia S, Jain A, et al. The materials application programming interface (api): a simple, flexible and efficient (api) for materials data based on representational state transfer (rest) principles. Comput Mater Sci. 2015;97:209–215.
  • Kresse G, Hafner J. Ab initio molecular dynamics for liquid metals. Phys Rev B. 1993;47:558–561.
  • Kresse G, Hafner J. Ab initio molecular-dynamics simulation of the liquid-metal-amorphous-semiconductor transition in germanium. Phys Rev B. 1994;49:14251–14269.
  • Kresse G, Furthm \~{J}. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput Mat Sci. 1996;6(1):15–50.
  • Kresse G, Furthmüller J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys Rev B. 1996;54:11169–11186.
  • Perdew JP, Burke K, Ernzerhof M. Generalized gradient approximation made simple. Phys Rev Lett. 1996;77:3865–3868.
  • Perdew JP, Burke K, Ernzerhof M. Generalized gradient approximation made simple [phys. rev. lett. 77, 3865 (1996)]. Phys Rev Lett. 1997;78:1396–1396.
  • Blöchl PE. Projector augmented-wave method. Phys Rev B. 1994;50:17953–17979.
  • Kresse G, Joubert D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys Rev B. 1999;59:1758–1775.
  • Murase Y, Uchitane T, Ito N. A tool for parameter-space explorations. Phys Procedia. 2014;57:73–76.
  • Terakura K, Hamada N, Oguchi T, et al. Local and non-local spin susceptibilities of transition metals. J Phys F Metal Phys. 1982;12(8):1661–1678.
  • Blum LC, Reymond JL. 970 million druglike small molecules for virtual screening in the chemical universe database gdb-13. J Am Chem Soc. 2009;131:8732–8733.