Materials Informatics: An Algorithmic Design Rule
This work addresses the problem of accelerating organic semiconductor discovery for the semiconductor industry, though it appears incremental as it builds on existing materials informatics approaches.
The researchers tackled the challenge of discovering novel organic semiconductors by applying materials informatics, combining fingerprinting of material properties with statistical inference and learning, and developed data-driven procedures using diverse neural network topologies, achieving advancements for the semiconductor industry and materials science community.
Materials informatics, data-enabled investigation, is a "fourth paradigm" in materials science research after the conventional empirical approach, theoretical science, and computational research. Materials informatics has two essential ingredients: fingerprinting materials proprieties and the theory of statistical inference and learning. We have researched the organic semiconductor's enigmas through the materials 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 materials science community. We have reviewed and corresponded with various algorithms for the neural network design topology for the materials informatics dataset.