Materials properties computational approaches

In such cases it is reasonable to step down to the molecular level of these materials and to think of a conjecture that many of the condensed materials properties may actually be connected to the properties of the individual macromolecules. Pursuing this idea one may follow two approaches. The first consists of molecular modeling of structures on a computer and simulating the material properties of interest. Alternatively, attempts can be made to set up a rigorous basic molecular theory. 

Both routes have their limitations. The basic theory of complex structures, which are encountered with macromolecules, often does not allow analytic solutions. Incisive, though reasonable, approximations have to be introduced. On the other hand, rigorous simulations can be made by means of molecular dynamics, but this technique has the limitation that only rather small and fast moving objects can be treated within a reasonable time, even with the fastest computers presently available. This minute scale gives valuable information on the local structure and local dynamics, but no reliable predictions of the macro-molecular properties can be made by this technique. All other simulations have to start with some basic assumptions. These in turn are backed by results obtained from basic theories. Hence both approaches are complementary and are needed when constructing a reliable framework for macromolecules that reflects the desired relation to the materials properties. 

While our theoretical understanding of the NLO properties of molecules is continually expanding, the development of empirical data bases of molecular structure-NLO property relationships is an important component of research in the field. Such data bases are important to the validation of theoretical and computational approaches to the prediction of NLO properties and are crucial to the evaluation of molecular engineering strategies seeking to identify the impact of tailored molecular structural variations on the NLO properties. These issues have led to a need for reliable and rapid determination of the NLO properties of bulk materials and molecules. 

The review is largely of rather traditional techniques — fragment methods and correlation between properties. More modem techniques based on wholly a priori computational approaches have not yet yielded methods that are robust in fact, much published material in this area is singularly unconvincing. Why should that be It is because physiochemical properties involve such matters as solvation and intermolecular forces that computational methods frequently fail the energy differences that need to be understood are small and not easily predicted computationally. 

S. Joseph Poon, Electronic and Thermoelectric Properties of Half-Heusler Alloys Terry M. Tritt, A. L. Pope, and J. W. Kolis, Overview of the Thermoelectric Properties of Quasicrystalline Materials and Their Potential for Thermoelectric Applications Alexander C. Ehrlich and Stuart A. Wolf, Military Applications of Enhanced Thermoelectrics David J. Singh, Theoretical and Computational Approaches for Identifying and Optimizing Novel Thermoelectric Materials... 

Since this approach does not account for long-range electrostatic potentials present in the extended material, the second approach chosen was the rigid-ion lattice energy minimization technique, widely used in solid-state chemistry. This method is based on the use of electrostatic potentials, as well as Born repulsion and bond-bending potentials parametrized such that computed atom—atom distances and angles and other material properties, such as, for instance, elastic constants, are well reproduced for related materials. In our case, parameters were chosen to fit a-quartz. 

Molecular modeling is a term used to describe a series of techniques that employ both quantum and molecular mechanics in conjunction with computer simulations to study the chemical and physical properties of a material (1). This approach offers a wide range of benefits to industry shortens product develojHnent cycles, reduces production costs, improves product properties and key solid-state structures, and properties that cannot be examined by experimental techniques may be predicted and studied by computer. Following is a brief review of the fundamentals of modeling crystalline systems. 

The most efficient method is to have the supplier use finite element analysis (FEA) to do a simulation. One can use trial and error approaches, but since the process involves cutting metal on a mold, it is faster and less expensive to use a computer simulation. The FEA program chosen must be capable of nonlinear calculations in order to properly model the nonlinear material properties. 

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