[HTML][HTML] Transformative strategies in photocatalyst design: merging computational methods and deep learning

J Liu, L Liang, B Su, D Wu, Y Zhang… - Journal of Materials …, 2024 - oaepublish.com
Photocatalysis is a unique technology that harnesses solar energy through in-situ
processes, operating without the need for external energy inputs. It is integral to advancing …

Experimental and AI-driven enhancements in gas-phase photocatalytic CO2 conversion over synthesized highly ordered anodic TiO2 nanotubes

MA Hossen, MM Hasan, Y Ahmed, A Abd Aziz… - Energy Conversion and …, 2025 - Elsevier
The photocatalytic hydrogenation of CO 2 to value-added products is one of the most
appealing sustainable strategies to meet growing fuel demand and lowering CO 2 levels in …

Machine Learning‐Assisted Design of Nitrogen‐Rich Covalent Triazine Frameworks Photocatalysts

M Wu, Z Song, Y Cui, Z Fu, K Hong, Q Li… - Advanced Functional …, 2024 - Wiley Online Library
Covalent triazine frameworks (CTFs), noted for their rich nitrogen content, have attracted
significant attention as promising photocatalysts. However, the structural complexity …

Advancing micro-nano supramolecular assembly mechanisms of natural organic matter by machine learning for unveiling environmental geochemical processes

M Zhang, Y Deng, Q Zhou, J Gao, D Zhang… - … Science: Processes & …, 2025 - pubs.rsc.org
The nano-self-assembly of natural organic matter (NOM) profoundly influences the
occurrence and fate of NOM and pollutants in large-scale complex environments. Machine …

In situ thermal-assisted photocatalytic decarboxylation of high-concentration biomass-derived fatty acids to alkanes

C Hao, G Guo, X Guo, S An - Renewable Energy, 2024 - Elsevier
Heating is a straightforward method to increase reaction rates to meet industrial production,
yet photocatalysis seldom incorporates it because their intrinsic driving force depends on the …

Interpretable Machine Learning for Accelerating Reverse Design and Optimizing CO2 Methanation Catalysts with High Activity at Low Temperatures

Q Yang, R Bao, D Rong, J **ao, J Zhou… - Industrial & …, 2024 - ACS Publications
CO2 methanation represents a promising technological pathway for achieving efficient
carbon dioxide resource utilization and mitigation of greenhouse gas emissions. However …

[HTML][HTML] Learning Effective Good Variables from Physical Data

G Barletta, G Trezza, E Chiavazzo - Machine Learning and Knowledge …, 2024 - mdpi.com
We assume that a sufficiently large database is available, where a physical property of
interest and a number of associated ruling primitive variables or observables are stored. We …

Supramolecular self-assembly of metal complex surfactants (MeCS) into micellar nanoscale reactors in aqueous solution

Y Chen, AM Ya'akobi, TV Nguyen, SC Kao… - Chemical …, 2025 - pubs.rsc.org
Surfactants are amphiphilic molecules that can form micellar structures with a hydrophobic
core and a hydrophilic corona in water. In this work, we combine the remarkable properties …

Machine learning models for easily obtainable descriptors of the electrocatalytic properties of Ag–Pd–Ir nanoalloys toward the formate oxidation reaction

X Liu, F Chen, W Zhang, F Ma, P Xu - Nanoscale, 2025 - pubs.rsc.org
Direct formate fuel cells (DFFCs) have received increasing attention due to their
environmentally benign and highly safe characteristics. However, the absence of highly …

[HTML][HTML] Progress in prediction of photocatalytic CO2 reduction using machine learning approach: A mini review

MM Ali, MA Hossen, A Abd Aziz - Next Materials, 2025 - Elsevier
The rapid progression of industrial revolution has contributed to an increase in greenhouse
gases (GHGs) emission, particularly carbon dioxide (CO 2), exacerbating global warming …