Deep Learning for Material Design

 Notice the advanced methods being used, like genetic algorithms to improve training.  Use for materials.  Material design continues to be a strong area of use.   See my 'materials' tag.  Technical.

Deep Learning Framework to Enable Material Design in Unseen Domain

KAIST (South Korea), September 29, 2021

Researchers at South Korea's KAIST and the University of California, Berkeley have developed a framework that uses a deep neural network to facilitate more efficient material or structure design beyond the domain of the initial training set. The method compensates for the weak predictive power of neural networks.

This involves three steps:

• using genetic algorithms to search for candidates with improved properties close to the training set, and mixing superior designs in the training set;

• determining whether the candidates actually have improved properties, and using data augmentation to duplicate validated designs and expand the training set; and

• using transfer learning to update the neural network with newly generated superior designs to broaden the reliable prediction domain.

Researchers are using the optimization framework to design metamaterial structures, segmented thermoelectric generators, and optimal sensor distributions.  ... "