AIMC Topic: Adsorption
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Prediction of fluid oil and gas volumes of shales with a deep learning model and its application to the Bakken and Marcellus shales.
Scientific reports
Dec 2, 2022
The fluid oil and gas volumes (S1) retained within the shales are one of the most important parameter of producible fluid oil and gas saturations of shales together with total organic carbon content. The S1 volumes can directly be obtained by Rock-Ev...
Universal machine-learning algorithm for predicting adsorption performance of organic molecules based on limited data set: Importance of feature description.
The Science of the total environment
Nov 17, 2022
Adsorption of organic molecules from aqueous solution offers a simple and effective method for their removal. Recently, there have been several attempts to apply machine learning (ML) for this problem. To this end, polyparameter linear free energy re...
Interpretable Deep Learning Model for Analyzing the Relationship between the Electronic Structure and Chemisorption Property.
The journal of physical chemistry letters
Sep 9, 2022
The use of machine learning (ML) is exploding in materials science as a result of its high predictive performance of material properties. Tremendous trainable parameters are required to build an outperforming predictive model, which makes it impossib...
Adsorbate-adsorbent potential energy function from second virial coefficient data: a non-linear Hopfield Neural Network approach.
Journal of molecular modeling
Sep 2, 2022
The Hopfield Neural Network has been successfully applied to solve ill-posed inverse problems in different fields of chemistry and physics. In this work, the non-linear approach for this method will be applied to retrieve the empirical parameters of ...
Machine learning analysis and prediction of N, NO, and O adsorption on activated carbon and carbon molecular sieve.
Environmental science and pollution research international
Aug 13, 2022
This research focuses on predicting the adsorbed amount of N, O, and NO on carbon molecular sieve and activated carbon using the artificial neural network (ANN) approach. Experimental isotherm data (data set 1242) on adsorbent type, gas type, tempera...
Optimization of Dechlorination Experiment Design Using Lightweight Deep Learning Model.
Computational intelligence and neuroscience
Jun 25, 2022
This exploration intends to remove chloride ions in production and life, enhance buildings' durability, and protect the natural environment from pollution. The current dechlorination technology is discussed based on the relevant theories, such as the...
A new active learning approach for adsorbate-substrate structural elucidation in silico.
Journal of molecular modeling
Jun 3, 2022
Adsorbate interactions with substrates (e.g. surfaces and nanoparticles) are fundamental for several technologies, such as functional materials, supramolecular chemistry, and solvent interactions. However, modeling these kinds of systems in silico, s...
Combined Deep Learning and Classical Potential Approach for Modeling Diffusion in UiO-66.
Journal of chemical theory and computation
Jun 2, 2022
Modeling of diffusion of adsorbates through porous materials with atomistic molecular dynamics (MD) can be a challenging task if the flexibility of the adsorbent needs to be included. This is because potentials need to be developed that accurately ac...
Towards universal neural network potential for material discovery applicable to arbitrary combination of 45 elements.
Nature communications
May 30, 2022
Computational material discovery is under intense study owing to its ability to explore the vast space of chemical systems. Neural network potentials (NNPs) have been shown to be particularly effective in conducting atomistic simulations for such pur...
Modeling of Remora Optimization with Deep Learning Enabled Heavy Metal Sorption Efficiency Prediction onto Biochar.
Chemosphere
May 23, 2022
Environmental distresses linked to heavy metal (HM) impurity in the water received significant attention among research communities. Recently, advancements in industrial sectors like paper industries, mining, non-ferrous metallurgy, electroplating, m...
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