Machine learning navigates vast materials space to discover new high-performance alloys

An active learning algorithm has discovered high-entropy versions of Invar alloys – materials widely used for scientific instruments and industrial transportation of liquefied gases because of their tiny thermal expansion. The technique could have significant potential for searching huge ranges of potential material compositions to find a small number with desirable properties.

The 1895 discovery of Invar (from invariant) – an iron–nickel alloy whose thermal expansion coefficient drops suddenly at a nickel composition of about 30–45% – earned Charles-Edouard Guillaume of the International Bureau of Weights and Measures the 1920 Nobel prize for physics. However, although it has exceptional thermal stability, its mechanical properties such as strength and ductility are less impressive. Alternative ‘Invar alloys’ are often prohibitively expensive or have other disadvantages.