Arsenic-MOF-LOMO v1.0.0: code, data and results for 'Random splits reward recognizing the adsorbent: a leave-one-MOF-out re-evaluation of a machine-learning model of arsenate adsorption on metal–organic frameworks'
Year: 2026
Type: software
Abstract: Code, data and results for a re-evaluation of a published LightGBM model of arsenate adsorption on eleven metal–organic frameworks (Abdi and Mazloom, Sci Rep 2022): random 85/15 splits, leave-one-MOF-out evaluation, diagnostics of what random splits reward, and four reference models.
Author: Monreal Hernández, Andrés
Institution: Universidad Estatal de Sonora
Language: English
Subfield: Environmental Chemistry
Field: Environmental Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7214521305