Density functional theory for chemical engineering: From capillarity to soft materials

AIChE Journal ◽  
2006 ◽  
Vol 52 (3) ◽  
pp. 1169-1193 ◽  
Author(s):  
Jianzhong Wu
Keyword(s):  
Density Functional Theory ◽  
Density Functional ◽  
Chemical Engineering ◽  
Soft Materials ◽  
Functional Theory

Experimental methods in chemical engineering: Density functional theory— DFT

2021 ◽  
Author(s):  
Hasan Al‐Mahayni ◽  
Xiao Wang ◽  
Jean‐Philippe Harvey ◽  
Gregory S. Patience ◽  
Ali Seifitokaldani
Keyword(s):  
Density Functional Theory ◽  
Density Functional ◽  
Chemical Engineering ◽  
Experimental Methods ◽  
Functional Theory

scholarly journals Van der Waals Density Functional Theory vdW-DFq for Semihard Materials

Crystals ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 243 ◽  
Author(s):  
Qing Peng ◽  
Guangyu Wang ◽  
Gui-Rong Liu ◽  
Suvranu De
Keyword(s):  
Density Functional Theory ◽  
Energetic Materials ◽  
Density Functional ◽  
Energetic Material ◽  
Van Der Waals ◽  
Molecular Crystals ◽  
Soft Materials ◽  
Dispersion Force ◽  
Electronic Charge ◽  
Functional Theory

There are a large number of materials with mild stiffness, which are not as soft as tissues and not as strong as metals. These semihard materials include energetic materials, molecular crystals, layered materials, and van der Waals crystals. The integrity and mechanical stability are mainly determined by the interactions between instantaneously induced dipoles, the so called London dispersion force or van der Waals force. It is challenging to accurately model the structural and mechanical properties of these semihard materials in the frame of density functional theory where the non-local correlation functionals are not well known. Here, we propose a van der Waals density functional named vdW-DFq to accurately model the density and geometry of semihard materials. Using β -cyclotetramethylene tetranitramine as a prototype, we adjust the enhancement factor of the exchange energy functional with generalized gradient approximations. We find this method to be simple and robust over a wide tuning range when calibrating the functional on-demand with experimental data. With a calibrated value q = 1.05 , the proposed vdW-DFq method shows good performance in predicting the geometries of 11 common energetic material molecular crystals and three typical layered van der Waals crystals. This success could be attributed to the similar electronic charge density gradients, suggesting a wide use in modeling semihard materials. This method could be useful in developing non-empirical density functional theories for semihard and soft materials.

scholarly journals Chemical engineering of adamantane by lithium functionalization: A first-principles density functional theory study

2011 ◽  
Vol 83 (11) ◽  
Author(s):  
Ahmad Ranjbar ◽  
Mohammad Khazaei ◽  
Natarajan Sathiyamoorthy Venkataramanan ◽  
Hoonkyung Lee ◽  
Yoshiyuki Kawazoe
Keyword(s):  
Density Functional Theory ◽  
First Principles ◽  
Density Functional ◽  
Chemical Engineering ◽  
Density Functional Theory Study ◽  
Functional Theory

scholarly journals Coarse-Grained Density Functional Theory Predictions via Deep Kernel Learning

2021 ◽  
Author(s):  
Nicholas Jackson ◽  
Ganesh Sivaraman
Keyword(s):  
Density Functional Theory ◽  
Active Learning ◽  
Density Functional ◽  
Predictive Accuracy ◽  
Coarse Graining ◽  
Soft Materials ◽  
Coarse Grained ◽  
Kernel Learning ◽  
Data Sets ◽  
Functional Theory

Scalable electronic predictions are critical for soft materials design. Recently, the Electronic Coarse-Graining (ECG) method was introduced to renormalize all-atom quantum chemical (QC) predictions to coarse-grained (CG) molecular representations using deep neural networks (DNN). While DNN can learn complex representations that prove challenging for traditional kernel-based methods, they are susceptible to overfitting and the overconfidence of uncertainty estimations. Here, we develop ECG within the GPU-accelerated Deep Kernel Learning (DKL) framework to enable CG QC predictions of a conjugated oligomer using range-separated hybrid density functional theory. DKL-ECG provides accurate reproduction of QC electronic properties in conjunction with prediction uncertainties that facilitate efficient training over multiple temperature data sets via active learning. We show that while active learning algorithms enable efficient sampling of a more diverse configurational space relative to random sampling, the predictive accuracy of DKL-ECG models is effectively identical across all active learning methodologies employed. We attribute this result to the low conformational barriers of our test molecule and the redundant sampling of configurations induced by Boltzmann sampling, even for distinct temperature ensembles.

Doping effects on the geometric and electronic structure of tin clusters

2019 ◽  
Vol 21 (44) ◽  
pp. 24478-24488 ◽  
Author(s):  
Martin Gleditzsch ◽  
Marc Jäger ◽  
Lukáš F. Pašteka ◽  
Armin Shayeghi ◽  
Rolf Schäfer
Keyword(s):  
Density Functional Theory ◽  
Electronic Structure ◽  
Density Functional ◽  
Beam Deflection ◽  
Functional Theory ◽  
Doping Effects ◽  
Depth Analysis ◽  
Trajectory Simulations

In depth analysis of doping effects on the geometric and electronic structure of tin clusters via electric beam deflection, numerical trajectory simulations and density functional theory.

What correlation effects are covered by density functional theory?

2000 ◽  
Vol 98 (20) ◽  
pp. 1639-1658 ◽  
Author(s):  
Yuan He, Jurgen Grafenstein, Elfi Kraka,
Keyword(s):  
Density Functional Theory ◽  
Density Functional ◽  
Correlation Effects ◽  
Functional Theory
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