| New tolerance factor to predict the stability of perovskite oxides and halides CJ Bartel, C Sutton, BR Goldsmith, R Ouyang, CB Musgrave, ... Science advances 5 (2), eaav0693, 2019 | 1333 | 2019 |
| Big data of materials science: critical role of the descriptor LM Ghiringhelli, J Vybiral, SV Levchenko, C Draxl, M Scheffler Physical review letters 114 (10), 105503, 2015 | 983 | 2015 |
| SISSO: a compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates R Ouyang, S Curtarolo, E Ahmetcik, M Scheffler, LM Ghiringhelli Physical Review Materials 2 (8), 083802, 2018 | 767 | 2018 |
| Report on the sixth blind test of organic crystal structure prediction methods AM Reilly, RI Cooper, CS Adjiman, S Bhattacharya, AD Boese, ... Structural Science 72 (4), 439-459, 2016 | 642 | 2016 |
| Insightful classification of crystal structures using deep learning A Ziletti, D Kumar, M Scheffler, LM Ghiringhelli Nature communications 9 (1), 2775, 2018 | 373 | 2018 |
| Improved long-range reactive bond-order potential for carbon. I. Construction JH Los, LM Ghiringhelli, EJ Meijer, A Fasolino Physical Review B—Condensed Matter and Materials Physics 72 (21), 214102, 2005 | 302 | 2005 |
| Simultaneous learning of several materials properties from incomplete databases with multi-task SISSO R Ouyang, E Ahmetcik, C Carbogno, M Scheffler, LM Ghiringhelli Journal of Physics: Materials 2 (2), 024002, 2019 | 186 | 2019 |
| Modeling the phase diagram of carbon LM Ghiringhelli, JH Los, EJ Meijer, A Fasolino, D Frenkel Physical review letters 94 (14), 145701, 2005 | 165 | 2005 |
| Learning physical descriptors for materials science by compressed sensing LM Ghiringhelli, J Vybiral, E Ahmetcik, R Ouyang, SV Levchenko, C Draxl, ... New Journal of Physics 19 (2), 023017, 2017 | 159 | 2017 |
| Roadmap on machine learning in electronic structure HJ Kulik, T Hammerschmidt, J Schmidt, S Botti, MAL Marques, M Boley, ... Electronic Structure 4 (2), 023004, 2022 | 154 | 2022 |
| Identifying domains of applicability of machine learning models for materials science C Sutton, M Boley, LM Ghiringhelli, M Rupp, J Vreeken, M Scheffler Nature communications 11 (1), 4428, 2020 | 146 | 2020 |
| GAtor: A first-principles genetic algorithm for molecular crystal structure prediction F Curtis, X Li, T Rose, A Vazquez-Mayagoitia, S Bhattacharya, ... Journal of chemical theory and computation 14 (4), 2246-2264, 2018 | 142 | 2018 |
| Towards efficient data exchange and sharing for big-data driven materials science: metadata and data formats LM Ghiringhelli, C Carbogno, S Levchenko, F Mohamed, G Huhs, ... npj computational materials 3 (1), 46, 2017 | 128* | 2017 |
| Theory and hierarchical calculations of the structure and energetics of [0001] tilt grain boundaries in graphene JM Carlsson, LM Ghiringhelli, A Fasolino Physical Review B—Condensed Matter and Materials Physics 84 (16), 165423, 2011 | 125 | 2011 |
| Uncovering structure-property relationships of materials by subgroup discovery BR Goldsmith, M Boley, J Vreeken, M Scheffler, LM Ghiringhelli New Journal of Physics 19 (1), 013031, 2017 | 121 | 2017 |
| Surface-induced crystallization in supercooled tetrahedral liquids T Li, D Donadio, LM Ghiringhelli, G Galli Nature materials 8 (9), 726-730, 2009 | 111 | 2009 |
| Artificial-intelligence-driven discovery of catalyst genes with application to CO2 activation on semiconductor oxides A Mazheika, YG Wang, R Valero, F Viñes, F Illas, LM Ghiringhelli, ... Nature Communications 13 (1), 419, 2022 | 105* | 2022 |
| Stability and Metastability of Clusters in a Reactive Atmosphere: <?format ?>Theoretical Evidence for Unexpected Stoichiometries of S Bhattacharya, SV Levchenko, LM Ghiringhelli, M Scheffler Physical review letters 111 (13), 135501, 2013 | 97 | 2013 |
| Competing adsorption between hydrated peptides and water onto metal surfaces: From electronic to conformational properties LM Ghiringhelli, B Hess, NFA van der Vegt, L Delle Site Journal of the American Chemical Society 130 (40), 13460-13464, 2008 | 77 | 2008 |
| Crowd-sourcing materials-science challenges with the NOMAD 2018 Kaggle competition C Sutton, LM Ghiringhelli, T Yamamoto, Y Lysogorskiy, L Blumenthal, ... npj Computational Materials 5 (1), 111, 2019 | 73* | 2019 |
Matthias SchefflerDirector of NOMAD Laboratory at FHI of Max-Planck-Gesellschaft確認したメール アドレス: fhi-berlin.mpg.de