Machine learning accelerates the investigation of targeted MOFs: performance prediction, rational design and intelligent synthesis
Metal-organic frameworks (MOFs) are a new class of nanoporous materials that are widely
used in various emerging fields due to their large specific surface area, high porosity and …
used in various emerging fields due to their large specific surface area, high porosity and …
Computational and Machine Learning Methods for CO2 Capture Using Metal–Organic Frameworks
H Mashhadimoslem, MA Abdol, P Karimi… - ACS …, 2024 - ACS Publications
Machine learning (ML) using data sets of atomic and molecular force fields (FFs) has made
significant progress and provided benefits in the fields of chemistry and material science …
significant progress and provided benefits in the fields of chemistry and material science …
Machine-learning models to predict hydrogen uptake of porous carbon materials from influential variables
Hydrogen (H 2) absorption percentage by porous carbon media (PCM) is important for
identifying efficient H 2 storage media. PCM with H 2-uptakes of greater than 5 wt% are …
identifying efficient H 2 storage media. PCM with H 2-uptakes of greater than 5 wt% are …
DFT-Quality adsorption simulations in metal–organic frameworks enabled by machine learning Potentials
R Goeminne, L Vanduyfhuys… - Journal of Chemical …, 2023 - ACS Publications
Nanoporous materials such as metal–organic frameworks (MOFs) have been extensively
studied for their potential for adsorption and separation applications. In this respect, grand …
studied for their potential for adsorption and separation applications. In this respect, grand …
Data driven discovery of MOFs for hydrogen gas adsorption
Hydrogen gas (H2) is a clean and renewable energy source, but the lack of efficient and cost-
effective storage materials is a challenge to its widespread use. Metal–organic frameworks …
effective storage materials is a challenge to its widespread use. Metal–organic frameworks …
Machine learning and deep learning for brain tumor MRI image segmentation
Brain tumors are often fatal. Therefore, accurate brain tumor image segmentation is critical
for the diagnosis, treatment, and monitoring of patients with these tumors. Magnetic …
for the diagnosis, treatment, and monitoring of patients with these tumors. Magnetic …
Recent progress on advanced solid adsorbents for CO2 capture: from mechanism to machine learning
Environmental pollution has become a serious issue due to the rapid development of
urbanization, industrialization, and vehicle traffic. Notably, fossil fuel combustion significantly …
urbanization, industrialization, and vehicle traffic. Notably, fossil fuel combustion significantly …
Enhancing wastewater treatment efficiency through machine learning-driven effluent quality prediction: A plant-level analysis
MAP Cechinel, J Neves, JVR Fuck… - Journal of Water …, 2024 - Elsevier
The main objective of this study was to develop, validate, and comprehend machine
learning (ML) models capable of predicting chemical oxygen demand concentration in the …
learning (ML) models capable of predicting chemical oxygen demand concentration in the …
Application of machine learning in material synthesis and property prediction
G Huang, Y Guo, Y Chen, Z Nie - Materials, 2023 - mdpi.com
Material innovation plays a very important role in technological progress and industrial
development. Traditional experimental exploration and numerical simulation often require …
development. Traditional experimental exploration and numerical simulation often require …
Engineering Porosity and Functionality in a Robust Twofold Interpenetrated Bismuth-Based MOF: Toward a Porous, Stable, and Photoactive Material
W A. Mohamed, J Chakraborty, L Bourda… - Journal of the …, 2024 - ACS Publications
Defect engineering in metal–organic frameworks (MOFs) has gained worldwide research
traction, as it offers tools to tune the properties of MOFs. Herein, we report a novel 2-fold …
traction, as it offers tools to tune the properties of MOFs. Herein, we report a novel 2-fold …