The endless search for better alloys.

The authors comment on a study that presents a physics-informed machine-learning approach for screening alloys with low thermal expansion coefficient within the iron-cobalt-nickel-chromium and iron-cobalt-nickel-chromium-copper composition space. They discuss a methodology developed by the researchers for training a machine-learning algorithm and used it to search for Invar materials, which have a very low coefficient of thermal expansion, among high-entropy alloys (HEA).