On the search for novel materials: insight and discovery through sharing of big data.
•
2 likes•3,374 views
Plenary lecture of the XIV SBPMat Meeting, given by Prof. Claudia Draxl (Humboldt University, Germany) on October 1, 2015, in Rio de Janeiro (Brazil).
Recommended
Recommended
More Related Content
What's hot
What's hot (20)
Viewers also liked
Viewers also liked (20)
Similar to On the search for novel materials: insight and discovery through sharing of big data.
Similar to On the search for novel materials: insight and discovery through sharing of big data. (20)
More from Sociedade Brasileira de Pesquisa em Materiais
More from Sociedade Brasileira de Pesquisa em Materiais (20)
Recently uploaded
Recently uploaded (20)
On the search for novel materials: insight and discovery through sharing of big data.
- 2. Samsung Galaxy Round Philips Lumiblade
- 5. Level Properties Methods Size I Atomic positions and nuclear charges, properties of free atoms, symmetry, temperature, pressure Input: definition of material „gene” 10 kB - 10 MB II Total energy, electron density, potential, wavefunctions, atomic forces, optimized geometry, elastic constants, etc. Density-functional theory (DFT) and ab initio molecular dynamics (MD) 10 MB - 10 TB III Excitation energies, dielectric screening, matrix elements of Coulomb interaction, etc. optical spectra, electrical conductivity, phonon spectra, thermal conductivity, etc. Many-body perturbation theory (MBPT), DF perturbation theory, and ab initio MD 1 GB - 1 TB IV Efficiency of solar cell, thermoelectric figure of merit, turn-over frequency of catalyst, etc. as a function of temperature and pressure Modeling, output derived from levels I-III „phenotype” 10 kB - 1 MB
- 10. The NoMaD Repository is a joint effort by the groups of Matthias Scheffler, FHI Berlin and Claudia Draxl, HU Berlin, and the Computer Center of the Max-Planck Society.
- 11. Feb 6, 2015: “… That data underlying scientific publications are not available for confirmatory analysis, reuse, and repurposing is an anachronism that we aim to address. …”
- 16. Arndt Bode LRZ Munich Alessandro De Vita Kings College London Claudia Draxl HU Berlin Daan Frenkel Univ. Cambridge Stefan Heinzel MPSCD Garching Francesc Illas Univ. Barcelona Kimmo Koski CSC Helsinki Jose Maria Cela BSC Barcelona Risto Nieminen Aalto Univ. Helsinki Ciaran Clissman Pintail Dublin Matthias Scheffler FHI Berlin Kristian Thygesen DTU Lyngby Angel Rubio MPSD Hamburg
- 19. Existing resources Conversion layers Big-data analytics WP4 VisualizeWP3 WP1 Search & retrieve WP2 WP7WP5 Dissemination & outreach WP2 WP1 WP6 HPC expertise & hardware
- 27. Calculate properties and functions, P, for many materials, i DFT Find the appropriate descriptor di; build a “table”: i di Pi Calculate properties and functions for new materials Find function P(d); do cross validation Statistical learning
- 28. Calculate properties and functions, P, for many materials, i DFT Find the appropriate descriptor di; build a “table”: i di Pi Calculate properties and functions for new materials Find function P(d); do cross validation Statistical learning
- 30. J. A. Van Vechten, PRB 182 , 891 (1969). J. C. Phillips, Rev. Mod. Phys. 42, 317 (1970). A. Zunger, PRB 22, 5839 (1980). D. G. Pettifor, Solid State Commun. 51, 31 (1984). Y. Saad, D. Gao, T. Ngo, S. Bobbitt, J. R. Chelikowsky, and W. Andreoni, PRB 85, 104104 (2012). L. Ghiringhelli J. Vybiral M. SchefflerS. Levchenko
- 32. d1 d2
- 33. zincblende rocksalt Eh, C related to band gap, dielectric constant, nearest-neighbor distance L.M. Ghiringhelli, J. Vybiral, S.V. Levchenko, C. Draxl, and M. Scheffler, PRL 114, 105503 (2015). J. A. Van Vechten, PRB 182 , 891 (1969). J. C. Phillips, RMP 42, 317 (1970).
- 34. L.M. Ghiringhelli, J. Vybiral, S.V. Levchenko, C. Draxl, and M. Scheffler, PRL 114, 105503 (2015). Free atoms Free dimers free atoms
- 36. zincblende rocksalt Eh, C related to band gap, dielectric constant, nearest-neighbor distance L.M. Ghiringhelli, J. Vybiral, S.V. Levchenko, C. Draxl, and M. Scheffler, PRL 114, 105503 (2015). Descriptor ZA, ZB Z*A, Z*B 1D 2D 3D 5D MAE 1*10-4 3*10-3 0.12 0.08 0.07 0.05 MaxAE 8*10-4 0.03 0.32 0.32 0.24 0.20 MAE, CV 0.13 0.14 0.12 0.09 0.07 0.05 MaxAE, CV 0.43 0.42 0.27 0.18 0.16 0.12
- 37. L.M. Ghiringhelli, J. Vybiral, S.V. Levchenko, C. Draxl, and M. Scheffler, PRL 114, 105503 (2015).