Functionality-driven material discovery is now a rapidly growing field as we are able to predict at the atomic scale the ground state and metastable structures of new materials with only the basic knowledge of their chemical composition.
Traditionally material discovery has been carried out experimentally, which is known to be a long and costly process. By combining the experience obtained through years of searching using known methods with computational simulation, researchers are now able to decrease the number of possibilities needed to be explored experimentally, therefore reducing time and cost.
The rise of computational simulations able to predict structural information gives researchers valuable insight into the material’s properties and functionalities. Predicting the structure of a new material efficiently has become one of the main focusses of the field, which looks to solve the structure prediction problem by effectively reducing the configuration space. It is also the subject of this Topical Review by Yanming Ma et al, the third highlight in my series on reviews published in JPCM this year.
As well as reducing the configuration space, Ma et al discuss the alternative avenue of enhancing the sampling efficiency on the potential energy surface. They discuss the development of their structural prediction method CALYPSO and the remaining challenges facing them.
One particular issue is in predicting more realistic systems, such as alloys and surfaces. These systems contain a large number of atoms in the simulation cell and so further development is needed to overcome the large computation cost involved in calculating the total energy minimization for each individual structure. For Ma et al’s solution to this particular problem, you can read the full review here.
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Image taken from Ma et al J. Phys.: Condens. Matter 27 203203. Copyright IOP Publishing 2015.
Categories: Journal of Physics: Condensed Matter