Chemical bonding, kinetics and the approach to equilibrium structures of simple metallic, molecular, and network microclusters

1986 ◽  
Vol 86 (3) ◽  
pp. 619-634 ◽  
Author(s):  
J. C. Phillips
Keyword(s):  
Chemical Bonding ◽  
Approach To Equilibrium ◽  
Equilibrium Structures ◽  
Bonding Kinetics

A molecular dynamics approach to equilibrium structures in crystals

2008 ◽  
Vol 64 (a1) ◽  
pp. C221-C221
Author(s):  
A.M. Reilly ◽  
D.A. Wann ◽  
C.A. Morrison ◽  
D.W.H. Rankin
Keyword(s):  
Molecular Dynamics ◽  
Approach To Equilibrium ◽  
Equilibrium Structures

Radiation-induced surface decomposition

1983 ◽  
Vol 41 ◽  
pp. 368-369
Author(s):  
M. L. Knotek
Keyword(s):  
Ionizing Radiation ◽  
Atomic Structure ◽  
Chemical Bonding ◽  
Neutral Species ◽  
Radiation Induced ◽  
Physical And Chemical ◽  
Surface Decomposition ◽  
The Relationship ◽  
Electron And Photon ◽  
Surface Bond

Modern surface analysis is based largely upon the use of ionizing radiation to probe the electronic and atomic structure of the surfaces physical and chemical makeup. In many of these studies the ionizing radiation used as the primary probe is found to induce changes in the structure and makeup of the surface, especially when electrons are employed. A number of techniques employ the phenomenon of radiation induced desorption as a means of probing the nature of the surface bond. These include Electron- and Photon-Stimulated Desorption (ESD and PSD) which measure desorbed ionic and neutral species as they leave the surface after the surface has been excited by some incident ionizing particle. There has recently been a great deal of activity in determining the relationship between the nature of chemical bonding and its susceptibility to radiation damage.

BAND NN: A Deep Learning Framework For Energy Prediction and Geometry Optimization of Organic Small Molecules

2019 ◽  
Author(s):  
Siddhartha Laghuvarapu ◽  
Yashaswi Pathak ◽  
U. Deva Priyakumar
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Computational Cost ◽  
Geometry Optimization ◽  
Dft Methods ◽  
Energy Prediction ◽  
Machine Learning Model ◽  
Equilibrium Structures ◽  
High Level ◽  
Non Equilibrium

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.

Approach to Equilibrium, the H-Theorem and Irreversibility

2018 ◽  
Author(s):  
Sauro Succi
Keyword(s):  
Boltzmann Equation ◽  
Detailed Knowledge ◽  
Approach To Equilibrium ◽  
The Boltzmann Equation ◽  
H Theorem ◽  
Irreversible Evolution

Like most of the greatest equations in science, the Boltzmann equation is not only beautiful but also generous. Indeed, it delivers a great deal of information without imposing a detailed knowledge of its solutions. In fact, Boltzmann himself derived most if not all of his main results without ever showing that his equation did admit rigorous solutions. This Chapter illustrates one of the most profound contributions of Boltzmann, namely the famous H-theorem, providing the first quantitative bridge between the irreversible evolution of the macroscopic world and the reversible laws of the underlying microdynamics.

scholarly journals Tunable thermo-reversible bicontinuous nanoparticle gel driven by the binary solvent segregation

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Yuyin Xi ◽  
Ronald S. Lankone ◽  
Li-Piin Sung ◽  
Yun Liu
Keyword(s):  
Phase Separation ◽  
Domain Size ◽  
Binary Solvent ◽  
Colloidal Systems ◽  
Colloidal Assembly ◽  
Structures And Properties ◽  
Wide Range ◽  
Equilibrium Process ◽  
Equilibrium Structures ◽  
Non Equilibrium

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.

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