Machine learning in solid heterogeneous catalysis: Recent developments, challenges and perspectives
Y Guan, D Chaffart, G Liu, Z Tan, D Zhang… - Chemical Engineering …, 2022 - Elsevier
Recently, the availability of extensive catalysis-related data generated by experimental data
and theoretical calculations has promoted the development of machine learning (ML) …
and theoretical calculations has promoted the development of machine learning (ML) …
A review on the different aspects and challenges of the dry reforming of methane (DRM) reaction
AGS Hussien, K Polychronopoulou - Nanomaterials, 2022 - mdpi.com
The dry reforming of methane (DRM) reaction is among the most popular catalytic reactions
for the production of syngas (H2/CO) with a H2: CO ratio favorable for the Fischer–Tropsch …
for the production of syngas (H2/CO) with a H2: CO ratio favorable for the Fischer–Tropsch …
Deep dive into machine learning density functional theory for materials science and chemistry
With the growth of computational resources, the scope of electronic structure simulations has
increased greatly. Artificial intelligence and robust data analysis hold the promise to …
increased greatly. Artificial intelligence and robust data analysis hold the promise to …
Perspective on computational reaction prediction using machine learning methods in heterogeneous catalysis
Heterogeneous catalysis plays a significant role in the modern chemical industry. Towards
the rational design of novel catalysts, understanding reactions over surfaces is the most …
the rational design of novel catalysts, understanding reactions over surfaces is the most …
Data-driven design of electrocatalysts: principle, progress, and perspective
To achieve carbon neutrality, electrocatalysis has the potential to be applied in the
technological upgrading of numerous industries. Therefore, the search for high-performance …
technological upgrading of numerous industries. Therefore, the search for high-performance …
Machine learning in experimental materials chemistry
B Selvaratnam, RT Koodali - Catalysis Today, 2021 - Elsevier
The development of advanced materials is an important aspect of modern life. However, the
discovery of novel materials involves searching the vast chemical space to find materials …
discovery of novel materials involves searching the vast chemical space to find materials …
[HTML][HTML] Computational modeling of green hydrogen generation from photocatalytic H2S splitting: Overview and perspectives
Hydrogen plays an important role in developing a clean and sustainable future energy
scenario. Substantial efforts to produce green hydrogen from water splitting, biomass and …
scenario. Substantial efforts to produce green hydrogen from water splitting, biomass and …
Graphs and Kernelized Learning Applied to Interactions of Hydrogen with Doped Gold Nanoparticle Electrocatalysts
A Pihlajamäki, S Malola… - The Journal of …, 2023 - ACS Publications
Understanding hydrogen adsorption on metal nanoparticles is a key prerequisite for
designing efficient electrocatalysts for water splitting and the hydrogen evolution reaction …
designing efficient electrocatalysts for water splitting and the hydrogen evolution reaction …
Evaluation of Machine Learning Models on Electrochemical CO2 Reduction Using Human Curated Datasets
BR Farris, T Niang-Trost, MS Branicky… - ACS Sustainable …, 2022 - ACS Publications
Machine learning holds the potential to be a powerful tool to aid in designing catalytic and
sustainable chemical systems. However, it is important for experimental researchers to …
sustainable chemical systems. However, it is important for experimental researchers to …
Machine learning approach for screening alloy surfaces for stability in catalytic reaction conditions
A catalytic surface should be stable under reaction conditions to be effective. However, it
takes significant effort to screen many surfaces for their stability, as this requires intensive …
takes significant effort to screen many surfaces for their stability, as this requires intensive …