scholarly journals Predicting Hydrophobicity by Learning Spatiotemporal Features of Interfacial Water Structure: Combining Molecular Dynamics Simulations with Convolutional Neural Networks

2020 ◽  
Vol 124 (41) ◽  
pp. 9103-9114 ◽  
Author(s):  
Atharva S. Kelkar ◽  
Bradley C. Dallin ◽  
Reid C. Van Lehn
Keyword(s):  
Neural Networks ◽  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Convolutional Neural Networks ◽  
Water Structure ◽  
Interfacial Water ◽  
Spatiotemporal Features ◽  
Interfacial Water Structure ◽  
Dynamics Simulations

scholarly journals Lauryl Phosphate Flotation Chemistry in Barite Flotation

Minerals ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 280
Author(s):  
Ying Lu ◽  
Weiping Liu ◽  
Xuming Wang ◽  
Huaigang Cheng ◽  
Fangqin Cheng ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Froth Flotation ◽  
Contrast Material ◽  
Water Structure ◽  
Phosphate Adsorption ◽  
Oil Well ◽  
Wide Ph Range ◽  
Interfacial Water Structure ◽  
Dynamics Simulations

Barite has numerous applications including barium mud for oil well drilling, manufacture of elemental barium, filler for paper and rubber industries, and contrast material for X-ray radiology for the digestive system. Currently, froth flotation is the main method for the beneficiation of barite using fatty acid as a typical collector. In this research, it was found that lauryl phosphate is also a promising collector for barite flotation. Results from microflotation, contact angle, and zeta potential indicate that lauryl phosphate is adsorbed on the barite surface and thus achieves superior flotation efficiency at a wide pH range. The interfacial water structure and wetting characteristics of barite surface with/without lauryl phosphate adsorption were also evaluated by molecular dynamics simulations (MDS). The results from molecular dynamics simulations and interaction energy calculations are in accord with the experimental results, which suggest that lauryl phosphate might be a potential collector for the flotation of barite.

scholarly journals Fast predictions of liquid-phase acid-catalyzed reaction rates using molecular dynamics simulations and convolutional neural networks

2020 ◽  
Vol 11 (46) ◽  
pp. 12464-12476 ◽  
Author(s):  
Alex K. Chew ◽  
Shengli Jiang ◽  
Weiqi Zhang ◽  
Victor M. Zavala ◽  
Reid C. Van Lehn
Keyword(s):  
Neural Networks ◽  
Molecular Dynamics ◽  
Liquid Phase ◽  
Molecular Dynamics Simulations ◽  
Convolutional Neural Networks ◽  
Reaction Rates ◽  
Acid Catalyzed ◽  
Dynamics Simulations

Solvent-mediated, acid-catalyzed reaction rates relevant to the upgrading of biomass into high-value chemicals are accurately predicted using a combination of molecular dynamics simulations and 3D convolutional neural networks.

scholarly journals Fast Predictions of Liquid-Phase Acid-Catalyzed Reaction Rates Using Molecular Dynamics Simulations and Convolutional Neural Networks

2020 ◽  
Author(s):  
Alex Chew ◽  
Shengli Jiang ◽  
Weiqi Zhang ◽  
Victor Zavala ◽  
Reid Van Lehn
Keyword(s):  
Neural Networks ◽  
Molecular Dynamics ◽  
Liquid Phase ◽  
Molecular Dynamics Simulations ◽  
Convolutional Neural Networks ◽  
Reaction Rates ◽  
Solvent Mixtures ◽  
Acid Catalyzed ◽  
Solvent Systems ◽  
Dynamics Simulations

The rates of liquid-phase, acid-catalyzed reactions relevant to the upgrading of biomass into high-value chemicals are highly sensitive to solvent composition and identifying suitable solvent mixtures is theoretically and experimentally challenging. We show that the atomistic configurations of reactant-solvent environments generated by classical molecular dynamics simulations can be exploited by 3D convolutional neural networks to enable fast predictions of Brønsted acid-catalyzed reaction rates for model biomass compounds. We develop a computational implementation, which we call SolventNet, and train it using experimental reaction data for seven biomass-derived oxygenates in water-cosolvent mixtures. We show that SolventNet can predict reaction rates for additional reactants and solvent systems an order of magnitude faster than prior simulation methods. This combination of machine learning with molecular dynamics enables the rapid screening of solvent systems and identification of improved biomass conversion conditions.

scholarly journals Fast Predictions of Liquid-Phase Acid-Catalyzed Reaction Rates Using Molecular Dynamics Simulations and Convolutional Neural Networks

2019 ◽  
Author(s):  
Alex Chew ◽  
Shengli Jiang ◽  
Weiqi Zhang ◽  
Victor Zavala ◽  
Reid Van Lehn
Keyword(s):  
Neural Networks ◽  
Molecular Dynamics ◽  
Liquid Phase ◽  
Molecular Dynamics Simulations ◽  
Convolutional Neural Networks ◽  
Reaction Rates ◽  
Solvent Mixtures ◽  
Acid Catalyzed ◽  
Solvent Systems ◽  
Dynamics Simulations

The rates of liquid-phase, acid-catalyzed reactions relevant to the upgrading of biomass into high-value chemicals are highly sensitive to solvent composition and identifying suitable solvent mixtures is theoretically and experimentally challenging. We show that the atomistic configurations of reactant-solvent environments generated by classical molecular dynamics simulations can be exploited by 3D convolutional neural networks to enable fast predictions of Brønsted acid-catalyzed reaction rates for model biomass compounds. We develop a computational implementation, which we call SolventNet, and train it using experimental reaction data for seven biomass-derived oxygenates in water-cosolvent mixtures. We show that SolventNet can predict reaction rates for additional reactants and solvent systems an order of magnitude faster than prior simulation methods. This combination of machine learning with molecular dynamics enables the rapid screening of solvent systems and identification of improved biomass conversion conditions.

scholarly journals Fast Predictions of Liquid-Phase Acid-Catalyzed Reaction Rates Using Molecular Dynamics Simulations and Convolutional Neural Networks

2020 ◽  
Author(s):  
Alex Chew ◽  
Shengli Jiang ◽  
Weiqi Zhang ◽  
Victor Zavala ◽  
Reid Van Lehn
Keyword(s):  
Neural Networks ◽  
Molecular Dynamics ◽  
Liquid Phase ◽  
Molecular Dynamics Simulations ◽  
Convolutional Neural Networks ◽  
Reaction Rates ◽  
Dynamics Simulation ◽  
Acid Catalyzed ◽  
Solvent Systems ◽  
Dynamics Simulations

The rates of liquid-phase, acid-catalyzed reactions relevant to the upgrading of biomass into high-value chemicals are highly sensitive to solvent composition and identifying suitable solvent mixtures is theoretically and experimentally challenging. We show that the complex atomistic configurations of reactant-solvent environments generated by classical molecular dynamics simulations can be exploited by 3D convolutional neural networks to enable fast predictions of Brønsted acid-catalyzed reaction rates for model biomass compounds. We develop a 3D convolutional neural network, which we call SolventNet, and train it to predict acid-catalyzed reaction rates using experimental reaction data and corresponding molecular dynamics simulation data for seven biomass-derived oxygenates in water-cosolvent mixtures. We show that SolventNet can predict reaction rates for additional reactants and solvent systems an order of magnitude faster than prior simulation methods. This combination of machine learning with molecular dynamics enables the rapid. high-throughput screening of solvent systems and identification of improved biomass conversion conditions.

PROPERTIES OF AQUEOUS SYSTEMS RELEVANT TO THE SCWR VIA MOLECULAR DYNAMICS SIMULATIONS

2015 ◽  
Vol 4 (1) ◽  
pp. 9-22 ◽  
Author(s):  
Dimitrios Kallikragas ◽  
David Guzonas ◽  
Igor Svishchev
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Supercritical Water ◽  
Iron Hydroxide ◽  
Water Structure ◽  
Heat Transfer Fluid ◽  
Surface Separation ◽  
Gen Iv ◽  
Dynamics Simulations ◽  
Supercritical Water Cooled Reactor

Supercritical water (SCW) is the intended heat transfer fluid in the proposed GEN-IV supercritical water cooled reactor (SCWR). The oxidative environment poses challenges in choosing appropriate design materials and understanding the behaviour of SCW at the nanoscale within crevices of the passivation layer is needed for developing a control strategy to minimize corrosion. Molecular dynamics simulations have been employed to investigate molecular structure and diffusion of water and chloride in nanometer-spaced iron hydroxide surfaces. Results demonstrate that water is more likely to accumulate on the surface at low-density conditions. The effect of confinement on the water structure diminishes with as little as 20 Å of surface separation. Clustering and the accumulation of water at the surface imply that the SCWR will be most susceptible to pitting corrosion and stress corrosion cracking. A parameterized equation is provided that gives the diffusion coefficients of O2, H2, and OH radical in high temperature and SCW.

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