Abstract
Structure–property relationships play a central role in condensed matter physics, chemistry, and materials science. However, the problem of predicting the structure of a material, given its chemical composition, remains immensely challenging. Here, we review some of the progress that has been made in this area for both crystalline materials and atomic clusters. Early work consisted of heuristic rules-of-thumb or structure maps using descriptors that were obtained largely by inspection. Increasingly, these approaches are being expanded to use descriptors that have been obtained by applying machine learning techniques to big data containing information from the experiment and/or first principles calculations. Improved techniques for global optimization in the multi-dimensional coordinate space have also led to major advances in the field.
Highlights
Structure–property relationships lie at the heart of materials science
Structure–property correlations are as important for nanosystems such as atomic clusters as they are for crystalline solids; the chemical reactivity and magnetic moment are two examples of properties that can vary significantly between structural isomers for a given cluster size
The problem is, that this crystal structure is not known a priori; the problem of predicting the crystal structure of a material given its chemical composition is possibly one of the hardest problems in materials science!11,12 How might one hope to approach this difficult problem? In this short perspective article, we briefly summarize some of the approaches that have been used in this context, starting from the most naïve brute force approaches
Summary
Structure–property relationships lie at the heart of materials science. It is a basic tenet of the field that the properties of a material depend crucially upon how its constituent atoms are arranged at the microscopic level. For a novel material whose existence is being theoretically predicted and has yet to be experimentally synthesized, obviously, no experimental information is available about its structure. First principles calculations, usually within the framework of ab initio density functional theory (DFT), are playing an important role in the field of materials science.9 They are usually faster and cheaper than performing experiments and (with improvements in theoretical methods), in general, quite accurate for most classes of materials.. The problem is, that this crystal structure is not known a priori; the problem of predicting the crystal structure of a material given its chemical composition is possibly one of the hardest problems in materials science!11,12 How might one hope to approach this difficult problem? We will conclude by taking stock of the progress made and discussing how much of a challenge structure prediction still poses today
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