A review of metal-organic frameworks and polymers in mixed matrix membranes for CO₂ capture

• Charlotte Skjold Qvist Christensen,
• Nicholas Hansen,
• Mahboubeh Motadayen,
• Nina Lock,
• Martin Lahn Henriksen and
• Jonathan Quinson

Beilstein J. Nanotechnol. 2025, 16, 155–186, doi:10.3762/bjnano.16.14

The round-robin approach applied to nanoinformatics: consensus prediction of nanomaterials zeta potential

• Dimitra-Danai Varsou,
• Arkaprava Banerjee,
• Joyita Roy,
• Kunal Roy,
• Giannis Savvas,
• Haralambos Sarimveis,
• Ewelina Wyrzykowska,
• Mateusz Balicki,
• Tomasz Puzyn,
• Georgia Melagraki,
• Iseult Lynch and
• Antreas Afantitis

Beilstein J. Nanotechnol. 2024, 15, 1536–1553, doi:10.3762/bjnano.15.121

• the physicochemical descriptors or biological activity of interest. Each ML algorithm has its strengths and weaknesses; thus, there is no universal solution for modelling regression or classification cases. The choice of the adequate ML method depends on the problem to be solved and the available data

On the additive artificial intelligence-based discovery of nanoparticle neurodegenerative disease drug delivery systems

• Shan He,
• Julen Segura Abarrategi,
• Harbil Bediaga,
• Sonia Arrasate and
• Humberto González-Díaz

Beilstein J. Nanotechnol. 2024, 15, 535–555, doi:10.3762/bjnano.15.47

• theory (PT) + machine learning (ML) algorithm [40]. In recent investigations, the IFPTML model has shown to be a powerful tool in molecular sciences and NDD research for the analysis of big datasets that include both structural and non-structural parameters. Application examples are drug screening

Prediction of cytotoxicity of heavy metals adsorbed on nano-TiO₂ with periodic table descriptors using machine learning approaches

• Joyita Roy,
• Souvik Pore and
• Kunal Roy

Beilstein J. Nanotechnol. 2023, 14, 939–950, doi:10.3762/bjnano.14.77

• present work manifests that ML in conjunction with periodic table descriptors can be used to explore the features and predict unknown compounds with similar properties. Keywords: heavy metals; HK-2 cell; ML algorithm; periodic table descriptors; QSAR; Introduction Nanoparticles (NPs) have gained much

• sorted response-based approach using the in-house dataset division tool (https://dtclab.webs.com/software-tools). In this study, the size ratio was set at 3:1 (training set/test set) for dataset division. In almost any ML algorithm, different models are trained for a dataset and the best-performing model

• ) In a similar manner as described in [32], another ensemble ML algorithm, XGBoost of tree boosting, uses a gradient-boosting framework for efficient and scalable implementation performance. Ensemble learning uses multiple predictions that are multiple models for gradient enhancement and yields good

The eNanoMapper database for nanomaterial safety information

• Nina Jeliazkova,
• Charalampos Chomenidis,
• Philip Doganis,
• Bengt Fadeel,
• Roland Grafström,
• Barry Hardy,
• Janna Hastings,
• Markus Hegi,
• Vedrin Jeliazkov,
• Nikolay Kochev,
• Pekka Kohonen,
• Cristian R. Munteanu,
• Haralambos Sarimveis,
• Bart Smeets,
• Pantelis Sopasakis,
• Georgia Tsiliki,
• David Vorgrimmler and
• Egon Willighagen

Beilstein J. Nanotechnol. 2015, 6, 1609–1634, doi:10.3762/bjnano.6.165

• , classification or clustering models, as well as cheminformatics algorithms, such as structure optimisation and descriptor calculation. A ML algorithm is made available as a web resource and a model is created by sending a HTTP POST to the algorithm URI, with specified dataset URI and modelling parameters, where