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IMAGE: Engineers at Rice University and Lawrence Livermore National Laboratory are using neural networks to accelerate the prediction of how microstructures of materials evolve. This example predicts snowflake-like dendritic crystal growth.. view more
Credit: Mesoscale Materials Science Group/Rice University
HOUSTON - (April 30, 2021) - 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.
In research published in âProceedings of the National Academy of Sciences,â scientists at Lawrence Livermore National Laboratory (LLNL) describe how liquid metals crystalize under immense pressure.
In 1879, Nobel-prize winning chemist Friedrich Wilhelm Ostwald discovered that liquids often first freeze into temporary, unstable structures before changing into their final, most stable equilibrium phase. This so-call âOstwald step ruleâ has been a fundamental mechanism for the study and synthesis of new materials and is a textbook principle that all physicists learn in school.
But in their study, LLNL researchers Babak Sadigh, Luis Zepeda-Ruiz and Jon Belof describe a new mechanism of solidification in copper that provides a more detailed analysis of the Ostwaldâs step rule and alters the fundamental understanding of nucleation under extreme pressure. They found that not only do metals first crystalized into an unstable or non-equilibrium phase, but that this