https://www.sciencedaily.com/releases/2019/04/190410125620.htm

Researchers at Rice University's Brown School of Engineering say they can find out fast by feeding basic details of their structures to "deep learning" agents that have the power to map the materials' properties. Better yet, the agents can quickly model materials scientists are thinking about making to facilitate the "bottom-up" design of 2D materials.

Rouzbeh Shahsavari, an assistant professor of civil and environmental engineering, and Rice graduate student Prabhas Hundi explored the capabilities of neural networks and multilayer perceptrons that take minimal data from the simulated structures of 2D materials and make "reasonably accurate" predictions of their physical characteristics, like strength, even after they're damaged by radiation and high temperatures.