857 citations · 1.8k across the 8 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…