Rigorous data curation, enrichment and meta-analysis enable autoML prediction of plant length responses to nanoparticles powered by the Enalos Cloud platform
A nanoparticle–plant dataset with atomistic descriptors was developed and used to train an optimized XGBoost model to predict nanoparticle effects on plant length without experimental data. Image designed with https://canva.com.
Authors
- Andreas Tsoumanis (ORCID: https://orcid.org/0000-0002-8512-4509)
- Nikoletta-Maria Koutroumpa (ORCID: https://orcid.org/0000-0002-8332-9690)
- Georgia Melagraki (ORCID: https://orcid.org/0000-0001-7547-2342)
- Anastasios G. Papadiamantis (ORCID: https://orcid.org/0000-0002-1297-3104)
- Antreas Afantitis (ORCID: https://orcid.org/0000-0002-0977-8180)
- Iseult Lynch
- Maria Antoniou
- Dimitra-Danai Varsou
- Aikaterini Theodori
- Dimitrios Zouraris
Institutions
- Hellenic Military Academy (GR)
- Cyprus Institute of Marketing (CY)
- Frederick University (CY)
- NovaMechanics (Cyprus) (CY)
- University of Birmingham (GB)
- University of Limassol (CY)
Publication Details
- Journal
- Environmental Science Nano
- Published
- 2025-12-22
- DOI
- https://doi.org/10.1039/d5en00897b
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- European Commission
- HORIZON EUROPE Framework Programme