scholarly journals Towards computational design of zeolite catalysts for CO2 reduction

RSC Advances ◽  
2015 ◽  
Vol 5 (55) ◽  
pp. 44361-44370 ◽  
Author(s):  
A. W. Thornton ◽  
D. A. Winkler ◽  
M. S. Liu ◽  
M. Haranczyk ◽  
D. F. Kennedy
Keyword(s):  
Machine Learning ◽  
Molecular Simulation ◽  
Co2 Reduction ◽  
Computational Design ◽  
Zeolite Catalysts ◽  
Structure Database ◽  
Computational Search ◽  
Reduction Catalysts

Computational search of structure database for CO2 reduction catalysts using molecular simulation and machine learning.

Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model

2019 ◽  
Author(s):  
Ryther Anderson ◽  
Achay Biong ◽  
Diego Gómez-Gualdrón
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Molecular Simulation ◽  
Large Scale ◽  
Learning Model ◽  
Operating Conditions ◽  
Small Subset ◽  
Screening Methods ◽  
Large Set ◽  
Metal Organic

<div>Tailoring the structure and chemistry of metal-organic frameworks (MOFs) enables the manipulation of their adsorption properties to suit specific energy and environmental applications. As there are millions of possible MOFs (with tens of thousands already synthesized), molecular simulation, such as grand canonical Monte Carlo (GCMC), has frequently been used to rapidly evaluate the adsorption performance of a large set of MOFs. This allows subsequent experiments to focus only on a small subset of the most promising MOFs. In many instances, however, even molecular simulation becomes prohibitively time consuming, underscoring the need for alternative screening methods, such as machine learning, to precede molecular simulation efforts. In this study, as a proof of concept, we trained a neural network as the first example of a machine learning model capable of predicting full adsorption isotherms of different molecules not included in the training of the model. To achieve this, we trained our neural network only on alchemical species, represented only by their geometry and force field parameters, and used this neural network to predict the loadings of real adsorbates. We focused on predicting room temperature adsorption of small (one- and two-atom) molecules relevant to chemical separations. Namely, argon, krypton, xenon, methane, ethane, and nitrogen. However, we also observed surprisingly promising predictions for more complex molecules, whose properties are outside the range spanned by the alchemical adsorbates. Prediction accuracies suitable for large-scale screening were achieved using simple MOF (e.g. geometric properties and chemical moieties), and adsorbate (e.g. forcefield parameters and geometry) descriptors. Our results illustrate a new philosophy of training that opens the path towards development of machine learning models that can predict the adsorption loading of any new adsorbate at any new operating conditions in any new MOF.</div>

scholarly journals Reaction-Based Machine Learning Representations for Predicting the Enantioselectivity of Organocatalysts

2021 ◽  
Author(s):  
Simone Gallarati ◽  
Raimon Fabregat ◽  
Ruben Laplaza ◽  
Sinjini Bhattacharjee ◽  
Matthew D. Wodrich ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Purity ◽  
Computational Design ◽  
Chiral Compounds ◽  
Significant Challenge

HHundreds of catalytic methods are developed each year to meet the demand for high-purity chiral compounds. The computational design of enantioselective organocatalysts remains a significant challenge, as catalysts are typically...

Machine-Learning-Guided Discovery and Optimization of Additives in Preparing Cu Catalysts for CO2 Reduction

2021 ◽  
Vol 143 (15) ◽  
pp. 5755-5762
Author(s):  
Ying Guo ◽  
Xinru He ◽  
Yuming Su ◽  
Yiheng Dai ◽  
Mingcan Xie ◽  
...  
Keyword(s):  
Machine Learning ◽  
Co2 Reduction ◽  
Guided Discovery ◽  
Cu Catalysts

Attainable Volumetric Targets for Adsorption-Based Hydrogen Storage in Porous Crystals: Molecular Simulation and Machine Learning

2018 ◽  
Vol 123 (1) ◽  
pp. 120-130 ◽  
Author(s):  
Grace Anderson ◽  
Benjamin Schweitzer ◽  
Ryther Anderson ◽  
Diego A. Gómez-Gualdrón
Keyword(s):  
Machine Learning ◽  
Hydrogen Storage ◽  
Molecular Simulation

Frontispiece: Judicious Ligand Design in Ruthenium Polypyridyl CO2 Reduction Catalysts to Enhance Reactivity by Steric and Electronic Effects

2016 ◽  
Vol 22 (42) ◽  
Author(s):  
Ben A. Johnson ◽  
Hemlata Agarwala ◽  
Travis A. White ◽  
Edgar Mijangos ◽  
Somnath Maji ◽  
...  
Keyword(s):  
Co2 Reduction ◽  
Ligand Design ◽  
Electronic Effects ◽  
Ruthenium Polypyridyl ◽  
Reduction Catalysts

Role of Pore Chemistry and Topology in the CO2Capture Capabilities of MOFs: From Molecular Simulation to Machine Learning

2018 ◽  
Vol 30 (18) ◽  
pp. 6325-6337 ◽  
Author(s):  
Ryther Anderson ◽  
Jacob Rodgers ◽  
Edwin Argueta ◽  
Achay Biong ◽  
Diego A. Gómez-Gualdrón
Keyword(s):  
Machine Learning ◽  
Molecular Simulation ◽  
And Topology
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