Lauri Himanen
Cited by
Cited by
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
Data‐Driven Materials Science: Status, Challenges, and Perspectives
L Himanen, A Geurts, AS Foster, P Rinke
Advanced Science 6 (21), 1900808, 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
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
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
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
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
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
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
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
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
Materials Informatics-Augmenting Materials Research with Data-driven Design and Machine Learning
L Himanen
Aalto University, 2020
Data-Driven Materials Science
L Himanen, A Geurts, AS Foster, P Rinke
Wiley, 2019
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
Hybrid Quantum Mechanical and Molecular Mechanical Modeling in Ion-Water Solutions
L Himanen
Kompleksiset verkostot: tapaustutkimus Helsingin ja Espoon seudun neurotieteilijöiden yhteistyöverkostosta
L Himanen
The system can't perform the operation now. Try again later.
Articles 1–16