Discovering materials for gas turbine engines through efficient predictive frameworks

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A team of researchers from the Department of Materials Science and Engineering at Texas A&M University, in conjunction with researchers from Ames National Laboratory, have developed an artificial intelligence framework capable of predicting high entropy alloys (HEAs) that can withstand extremely high temperature, oxidizing environments. This method could significantly reduce the time and costs of finding alloys by decreasing the number of experimental analyses required. This research was recently published in Material Horizons.

The joint work by Texas A&M and Ames National Laboratory was supported by the Ultrahigh Temperature Impervious Materials Advancing Turbine Efficiency Program of the Advanced Research Projects Agency-Energy. The National Science Foundation and the U.S. Department of Energy (Basic Energy Science and Fossil Energy program) also supported this work.

News Link: Discovering materials for gas turbine engines through efficient predictive frameworks