Materials Informatics: An Algorithmic Design Rule

We have researched the organic semiconductor's enigmas through the material informatics approach. By applying diverse neural network topologies, logical axiom, and inferencing information science, we have developed data-driven procedures for novel organic semiconductor discovery for the semiconductor industry and knowledge extraction for the material science community. We have reviewed and corresponded with various algorithms for the neural network design topology for the material informatics dataset, as shown in Figure 1, a generalized neural network topology. We have used four chemical compound space databases for model training and validation in this research notebook. The first one is the general quantum chemistry structures and properties of 134-kilo molecules (QM9) of computed geometric, energetic, electronic, and thermodynamic properties for 134-kilo stable small organic molecules made up of C, H, O, N, F for the novel design of new drugs and materials.