857 citations · 1.8k across the 8 of their papers we have counts for
8 papers
ML-guided screening of chalcogenide perovskites as solar energy materials
Diego A. Garzón, Lauri Himanen, Luisa Andrade +2
Chalcogenide perovskites have emerged as promising absorber materials for next-generation photovoltaic devices, yet their experimental realization remains limited by competing phas…
An autonomous living database for perovskite photovoltaics
Sherjeel Shabih, Hampus Näsström, Sharat Patil +16
Scientific discovery is severely bottlenecked by the inability of manual curation to keep pace with exponential publication rates. This creates a widening knowledge gap. This is es…
Updates to the DScribe Library: New Descriptors and Derivatives
Jarno Laakso, Lauri Himanen, Henrietta Homm +4
We present an update of the DScribe package, a Python library for atomistic descriptors. The update extends DScribe's descriptor selection with the Valle-Oganov materials fingerpri…
Data-driven materials science: status, challenges and perspectives
Lauri Himanen, Amber Geurts, Adam S. Foster +1
Data-driven science is heralded as a new paradigm in materials science. In this field, data is the new resource, and knowledge is extracted from materials data sets that are too bi…
DScribe: Library of Descriptors for Machine Learning in Materials Science
Lauri Himanen, Marc O. J. Jäger, Eiaki V. Morooka +5
DScribe is a software package for machine learning that provides popular feature transformations ("descriptors") for atomistic materials simulations. DScribe accelerates the applic…
Database-driven high-throughput study for hybrid perovskite coating materials
Azimatu Seidu, Lauri Himanen, Jingrui Li +1
We developed a high-throughput screening scheme to acquire candidate coating materials for hybrid perovskites. From more than 1.8 million entries of an inorganic compound database,…
Chemical diversity in molecular orbital energy predictions with kernel ridge regression
Annika Stuke, Milica Todorović, Matthias Rupp +4
Instant machine learning predictions of molecular properties are desirable for materials design, but the predictive power of the methodology is mainly tested on well-known benchmar…
Understanding doped perovskite ferroelectrics with defective dipole model
J. Liu, L. Jin, Z. Jiang +6
While doping is widely used for tuning physical properties of perovskites in experiments, it remains a challenge to exactly know how doping achieves the desired effects. Here, we p…