Machine learning could vastly speed up the search for new metals

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"... The team was hunting for metals with a low level of “invar,” which refers to how much materials expand or contract when exposed to high or low temperatures. 

Metals with low invar don’t change size under extreme temperatures. They are commonly used in industries where that property is useful—for example, the transportation and storage of natural gas ..."

"Researchers and engineers are constantly searching for materials with specific properties to drive the rapid development of various technologies. Because of the practically infinite combinations of materials, these searches need to be strategized. ... More recently, researchers have ventured into looking for alloys with multiple principal elements. This type of alloy, called a high-entropy alloy (HEA), greatly expands the search space of alloys for materials design. ... present a physics-informed machine-learning approach to screen alloys with low thermal expansion coefficient within the huge iron-cobalt-nickel-chromium (Fe–Co–Ni–Cr) and iron-cobalt-nickel-chromium-copper (Fe–Co–Ni–Cr–Cu) composition space. These materials, which expand and contract very little with temperature changes, make them valuable for application on precision instruments for which high dimensional stability of the components is required."

"Invar alloys have extremely low thermal expansion, making them attractive for several types of applications. Finding these types of alloys in a complex compositional space, however, is challenging. ... The alloys are both compositionally complex, high entropy materials, thus demonstrating the power of this approach for materials discovery."

From the abstract:

"High-entropy alloys are solid solutions of multiple principal elements that are capable of reaching composition and property regimes inaccessible for dilute materials. Discovering those with valuable properties, however, too often relies on serendipity, because thermodynamic alloy design rules alone often fail in high-dimensional composition spaces. We propose an active learning strategy to accelerate the design of high-entropy Invar alloys in a practically infinite compositional space based on very sparse data. Our approach works as a closed-loop, integrating machine learning with density-functional theory, thermodynamic calculations, and experiments. After processing and characterizing 17 new alloys out of millions of possible compositions, we identified two high-entropy Invar alloys with extremely low thermal expansion coefficients around 2 × 10−6 per degree kelvin at 300 kelvin. We believe this to be a suitable pathway for the fast and automated discovery of high-entropy alloys with optimal thermal, magnetic, and electrical properties."

Machine learning could vastly speed up the search for new metals

The endless search for better alloys Machine learning narrows down the enormous search space for functional materials

Machine learning–enabled high-entropy alloy discovery (no public access)