scholarly journals Equilibrium Structures and Stationary Patterns on Magnetic Colloidal Fluids

Author(s):  
Ricardo Peredo Ortíz ◽  
Martin Hernández Contreras ◽  
Raquel Hernández Gómez
2019 ◽  
Author(s):  
Siddhartha Laghuvarapu ◽  
Yashaswi Pathak ◽  
U. Deva Priyakumar

Recent advances in artificial intelligence along with development of large datasets of energies calculated using quantum mechanical (QM)/density functional theory (DFT) methods have enabled prediction of accurate molecular energies at reasonably low computational cost. However, machine learning models that have been reported so far requires the atomic positions obtained from geometry optimizations using high level QM/DFT methods as input in order to predict the energies, and do not allow for geometry optimization. In this paper, a transferable and molecule-size independent machine learning model (BAND NN) based on a chemically intuitive representation inspired by molecular mechanics force fields is presented. The model predicts the atomization energies of equilibrium and non-equilibrium structures as sum of energy contributions from bonds (B), angles (A), nonbonds (N) and dihedrals (D) at remarkable accuracy. The robustness of the proposed model is further validated by calculations that span over the conformational, configurational and reaction space. The transferability of this model on systems larger than the ones in the dataset is demonstrated by performing calculations on select large molecules. Importantly, employing the BAND NN model, it is possible to perform geometry optimizations starting from non-equilibrium structures along with predicting their energies.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Yuyin Xi ◽  
Ronald S. Lankone ◽  
Li-Piin Sung ◽  
Yun Liu

AbstractBicontinuous porous structures through colloidal assembly realized by non-equilibrium process is crucial to various applications, including water treatment, catalysis and energy storage. However, as non-equilibrium structures are process-dependent, it is very challenging to simultaneously achieve reversibility, reproducibility, scalability, and tunability over material structures and properties. Here, a novel solvent segregation driven gel (SeedGel) is proposed and demonstrated to arrest bicontinuous structures with excellent thermal structural reversibility and reproducibility, tunable domain size, adjustable gel transition temperature, and amazing optical properties. It is achieved by trapping nanoparticles into one of the solvent domains upon the phase separation of the binary solvent. Due to the universality of the solvent driven particle phase separation, SeedGel is thus potentially a generic method for a wide range of colloidal systems.


Nature ◽  
2021 ◽  
Vol 590 (7845) ◽  
pp. 268-274 ◽  
Author(s):  
Haridas Mundoor ◽  
Jin-Sheng Wu ◽  
Henricus H. Wensink ◽  
Ivan I. Smalyukh
Keyword(s):  

2021 ◽  
Vol 154 (19) ◽  
pp. 194302
Author(s):  
Jean Demaison ◽  
Natalja Vogt ◽  
Yan Jin ◽  
Rizalina Tama Saragi ◽  
Marcos Juanes ◽  
...  

1988 ◽  
Vol 60 (4) ◽  
pp. 271-274 ◽  
Author(s):  
Pietro Ballone ◽  
Wanda Andreoni ◽  
Roberto Car ◽  
Michele Parrinello

1991 ◽  
Vol 150 (2) ◽  
pp. 493-510 ◽  
Author(s):  
M. Le Guennec ◽  
W. Chen ◽  
G. Wlodarczak ◽  
J. Demaison ◽  
R. Eujen ◽  
...  

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