Work

Rigorous data curation, enrichment and meta-analysis enable autoML prediction of plant length responses to nanoparticles powered by the Enalos Cloud platform

Year: 2025

Type: article

Abstract: 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.

Source: Environmental Science Nano

DOI: 10.1039/d5en00897b

Authors: Dimitra-Danai Varsou, Aikaterini Theodori, Anastasios G. Papadiamantis, Andreas Tsoumanis, Dimitrios Zouraris, Maria Antoniou, Nikoletta-Maria Koutroumpa, Georgia Melagraki, Iseult Lynch, Antreas Afantitis

Institutions: NovaMechanics (Cyprus), Hellenic Military Academy, University of Birmingham, Frederick University

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: European Commission, HORIZON EUROPE Framework Programme, Innovate UK, Horizon 2020

Awards: G3717606236, G3859236714, Accessible Innovative Methods for the Safety & Sustainability Assessment of Chemicals & Materials, CHIASMA: Accessible Innovative Methods for the Safety & Sustainability Assessment of Chemicals & Materials

OpenAlex ID: W7116707013

FWCI: 0.414

Cites: 85

Cited by: 2

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