Metallurgy and material design have thousands of years' history and have played a critical role in the civilization process of humankind. The traditional trial-and-error method has been unprecedentedly challenged in the modern era when the number of components and phases in novel alloys keeps increasing, with high-entropy alloys as the representative. New opportunities emerge for alloy design in the artificial intelligence era. Here, a successful machine-learning (ML) method has been developed to identify the microstructure images with eye-challenging morphology for a number of martensitic and ferritic steels. Assisted by it, a new neural-network method is proposed for the inverse design of alloys with 20 components, which can accelerate the design process based on microstructure. The method is also readily applied to other material systems given sufficient microstructure images. This work lays the foundation for inverse alloy design based on microstructure images with extremely similar features.

The fatigue crack growth rates for nickel-based superalloy Haynes 282 were measured at temperatures of 550, 650, and 750°C using compact tension specimens with a load ratio of 0.1 and cyclic loading frequencies of 25 Hz and 0.25 Hz. Increasing the temperature from 550 to 750°C caused the fatigue crack growth rates to increase from $20 to 60% depending upon the applied stress intensity level. The effect of reducing the applied loading frequency increased the fatigue crack growth rates from $20 to 70%, also depending upon the applied stress intensity range. The crack path was observed to be transgranular for the temperatures and frequencies used during fatigue crack growth rate testing. At 750°C, there were some indications of limited intergranular cracking excursions at both loading frequencies; however, the extent of intergranular crack growth was limited and the cause is not understood at this time.

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