Scientists Use Neural Networks to Rethink Material Design
HOUSTON – The microscopic structures and properties of materials are intimately linked, and customizing them is a challenge. Rice University engineers are determined to simplify the process through machine learning. To that end, the Rice lab of materials scientist Ming Tang, in collaboration with physicist Fei Zhou at Lawrence Livermore National Laboratory, introduced a technique to predict the evolution of microstructures — structural features between 10 nanometers and 100 microns — in materials.
Their open-access paper in the Cell Press journal Patterns shows how neural networks (computer models that mimic the brain’s neurons) can train themselves to predict how a structure will grow under a certain environment, much like a snowflake forms from moisture in nature. In fact, snowflake-like, dendritic crystal structures were one of the examples the lab used in its proof-of-concept study.