14 Mar 2026Open access
A hybrid DFT–machine learning approach for fast HOMO–LUMO gap prediction in naphthalene diimide MOF linkersPublisher siteMaterials Chemistry and Physics, Metal-Organic Frameworks: Synthesis and Applications
Active, last published 2026
Sofiene
Achour
Scholar“Research profile drawn from open scholarly records.”
University of Tunis El Manar
Tunisia
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01 · About the work
What I study
research summary for Sofiene Achour will appear here once their profile is updated.
02 · Reach
Publication output and reach
Sofiene's work has been cited 45 times across 4 active years. Bars show publications per year; the line shows cumulative citation totals across publications first appearing in each year.
04 · Where I am
Affiliations
Affiliation on published researchTunis El Manar University, TunisiaDrawn from the institution listed on this researcher’s published work.
Research themes (UN SDGs)
05 · Selected work
Recent publications
11 works10 Jun 2025Open access2cites
Predictive Modeling of Yield Sooting Index Using Machine Learning with Uncertainty EstimationPublisher siteACS Omega, Manufacturing Process and Optimization
19 May 2025Open access19cites
Machine learning-driven nanoparticle toxicityPublisher siteEcotoxicology and Environmental Safety, Nanoparticles: synthesis and applications
9 Jan 2025Open access5cites
Specific surface area (SSA) of perovskites with uncertainty estimation approachPublisher siteComputational Materials Science, Machine Learning in Materials Science
1 Jan 2025Open access
A Hybrid DFT-Machine Learning Approach for Fast HOMO–LUMO Gap Prediction in Naphthalene Diimide MOF LinkersPublisher siteSSRN Electronic Journal, Metal-Organic Frameworks: Synthesis and Applications
2 Oct 2024Open access13cites
<scp>ML</scp> ‐driven models for predicting <scp> CO <sub>2</sub> </scp> uptake in metal–organic frameworks ( <scp>MOFs</scp> )Publisher siteThe Canadian Journal of Chemical Engineering, Metal-Organic Frameworks: Synthesis and Applications
23 Jul 2024Open access
Hydrogenation of hexene catalyzed by a ruthenium (<scp>II</scp>) complex with <i>N</i>‐heterocyclic carbene ligandsPublisher siteInternational Journal of Quantum Chemistry, Asymmetric Hydrogenation and Catalysis
14 Mar 2024Open access1cites
Machine Learning-driven models for predicting CO2 Uptake in Metal-Organic Frameworks (MOFs)Publisher siteResearch Square, Metal-Organic Frameworks: Synthesis and Applications
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