Computational Design of Materials

Traditional scientific disciplines such as chemistry and solid-state physics have so far made much use of computational methods to investigate the structural and electronic properties of existing materials [14,43]. These methods are mainly of two kinds: quantum mechanical methods that attempt to reach an approximate solution of the Schrodinger equation describing the physics of the atoms in the material, and methods based on atom-atom potentials and force-fields that are capable of modeling the chemical constituents of materials by employing the laws of classical mechanics. Each method has its advantages and disadvantages and the choice of one with respect to the other is often dictated by the specific problems and systems under investigation.

In addition to allowing the study of properties of existing materials, however, computational methods are an increasingly important tool in the hands of researchers for both property prediction and materials design [44-46]. Jansen and Schon [46] have recently proposed a general approach for the prediction of not-yet-synthesized materials that is based on the global exploration of the energy landscape of a chemical system. First, candidate structures are identified and their kinetic and thermodynamic stabilities are computationally assessed. Then, local free energies and physical properties are computed for a limited set of stable structures. In the final step, the candidates with the desired properties are selected. An important observation made by these authors concerns the applicability of their approach to materials that are capable of existence. In other words, materials design is not free but constrained by the building blocks that Nature offers.

In the spirit of Jansen and Schon's approach, we propose the general scheme for the design of nanostructured materials shown in Figure 11.18. The starting point

Figure 11.18. Proposed approach for the design of nanostructured materials.

of the flow chart is represented by the molecule whose structure can be obtained from a database of crystal structures such as CSD [12]. Molecules can be synthesized and modified according to the needs of a given design project. This can be achieved with the aid of a database of chemical reactions. Subsequently, molecular nanostructures with sizes larger than 1 nm are constructed either by assembling a discrete number of molecules or by attaching large chemical groups to a parent molecule (chemical functionalization). We call this step covalent assembly. It is here where computational quantum chemistry methods [14] can be highly effective in predicting the stability of the designer polyatomic system (molecular nanostruc-ture). In this regard, we recently predicted a novel series of compact hydrocarbons obtained from the covalent assembly of cubane units [47]. These novel molecular nanostructures, which we dubbed cubanoids, were proposed to be the precursors of superhard materials. A similar approach could also be applied to the polyhedral heteroborane clusters discussed above.

The next and last step of Figure 11.18 represents the one leading to the bulk nanostructured material by self-assembly via noncovalent interactions. These comprise hydrogen bonds, Coulombic (ion-ion, ion-dipole, and dipole-dipole interactions) and van der Waals interactions. The possibility of automating the self-assembly step would be useful in extending the search for novel architectures. In this regard, Mellot-Draznieks et al. [48] have recently proposed an interesting approach based on the automated assembly of building units leading to hybrid organic-inorganic frameworks. The successful prediction of novel topologies can be partly ascribed to the use of fragments containing transition metal ions that are capable of forming directional metal-ligand bonds.

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