| DScribe: Library of descriptors for machine learning in materials science L Himanen, MOJ Jäger, EV Morooka, FF Canova, YS Ranawat, DZ Gao, ... Computer Physics Communications 247, 106949, 2020 | 713 | 2020 |
| Data‐Driven Materials Science: Status, Challenges, and Perspectives L Himanen, A Geurts, AS Foster, P Rinke Advanced Science 6 (21), 1900808, 2019 | 692 | 2019 |
| Machine learning hydrogen adsorption on nanoclusters through structural descriptors MOJ Jäger, EV Morooka, F Federici Canova, L Himanen, AS Foster npj Computational Materials 4 (1), 37, 2018 | 198 | 2018 |
| Chemical diversity in molecular orbital energy predictions with kernel ridge regression A Stuke, M Todorović, M Rupp, C Kunkel, K Ghosh, L Himanen, P Rinke The Journal of chemical physics 150 (20), 2019 | 85 | 2019 |
| NOMAD: A distributed web-based platform for managing materials science research data M Scheidgen, L Himanen, AN Ladines, D Sikter, M Nakhaee, Á Fekete, ... Journal of Open Source Software 8 (90), 5388, 2023 | 43 | 2023 |
| Updates to the DScribe library: New descriptors and derivatives J Laakso, L Himanen, H Homm, EV Morooka, MOJ Jäger, M Todorović, ... The Journal of Chemical Physics 158 (23), 2023 | 36 | 2023 |
| Understanding doped perovskite ferroelectrics with defective dipole model J Liu, L Jin, Z Jiang, L Liu, L Himanen, J Wei, N Zhang, D Wang, CL Jia The Journal of chemical physics 149 (24), 2018 | 18 | 2018 |
| Materials structure genealogy and high-throughput topological classification of surfaces and 2D materials L Himanen, P Rinke, AS Foster npj Computational Materials 4 (1), 52, 2018 | 10 | 2018 |
| Database-driven high-throughput study of coating materials for hybrid perovskites A Seidu, L Himanen, J Li, P Rinke New Journal of Physics 21 (8), 083018, 2019 | 8* | 2019 |
| An object oriented Python interface for atomistic simulations T Hynninen, L Himanen, V Parkkinen, T Musso, J Corander, AS Foster Computer Physics Communications 198, 230-237, 2016 | 4 | 2016 |
| Development of a FAIR Data Management Infrastructure S Shabih, M Kühbach, M Scheidgen, L Himanen, S Brockhauser, B Haas, ... Microscopy and Microanalysis 28 (S1), 2930-2932, 2022 | 3 | 2022 |
| Materials Informatics-Augmenting Materials Research with Data-driven Design and Machine Learning L Himanen Aalto University, 2020 | 2 | 2020 |
| Introducing a FAIR RDM infrastructure for electron microscopy and other materials science data C Koch, M Kühbach, S Shabih, S Brockhauser, E Spiecker, M Scheidgen, ... BIO Web of Conferences 129, 10024, 2024 | | 2024 |
| Correction to: Data-Driven Materials Science: Status, Challenges, and Perspectives (Advanced Science,(2019), 6, 21,(1900808), 10.1002/advs. 201900808) L Himanen, A Geurts, AS Foster, P Rinke Advanced Science 7 (2), 2020 | | 2020 |
| Hybrid Quantum Mechanical and Molecular Mechanical Modeling in Ion-Water Solutions L Himanen | | 2015 |
| Kompleksiset verkostot: tapaustutkimus Helsingin ja Espoon seudun neurotieteilijöiden yhteistyöverkostosta L Himanen | | 2012 |
Adam S. FosterAalto UniversityVerified email at aalto.fi