Contributed by Alán Aspuru-Guzik
What brings a chemist to post about a joint paper of a professor from a school of pharmacy and one from a department of materials science? A great multidisciplinary paper!
This paper is a tour de force for computational materials science. The authors, from the Tropsha and Curtarolo groups have applied state-of-the-art tools from chemoinformatics and machine learning to the challenging problem of materials design. In this paper, led by Olexandr Isayev, the authors employ chemical descriptors based on electronic properties such as the density of states, as well as local properties such as the modified simplex approach to catalog materials and their properties. They apply the methods to the large datasets compiled by the Curtarolo group and find very interesting domains of material space once they apply the Tanimoto similarity metric.
The authors generate networks that they call materials carotgrams, where the nodes are compounds and the connections are the similarities between them. It is nice to see that naturally, regions of similar physicochemical properties emerge from their analysis.
As with all these methods, the proof is in the pudding, and the challenge to this great collaboration is to propose a material that has not been synthesized yet, and show it has better properties than any other material out there.