A combined experimental\u2013computational methodology for accelerated design of AlNiCo-type permanent
magnetic alloys is presented with the objective of simultaneously extremizing several magnetic
properties. Chemical concentrations of eight alloying elements were initially generated using a quasirandom
number generator so as to achieve a uniform distribution in the design variable space. It was
followed by manufacture and experimental evaluation of these alloys using an identical thermo-magnetic
protocol. These experimental data were used to develop meta-models capable of directly relating
the chemical composition with desired macroscopic properties of the alloys. These properties were
simultaneously optimized to predict chemical compositions that result in improvement of properties.
These data were further utilized to discover various correlations within the experimental dataset by using
several concepts of artificial intelligence. In this work, an unsupervised neural network known as selforganizing
maps was used to discover various patterns reported in the literature. These maps were also
used to screen the composition of the next set of alloys to be manufactured and tested in the next
iterative cycle. Several of these Pareto-optimized predictions out-performed the initial batch of alloys.
This approach helps significantly reducing the time and the number of alloys needed in the alloy
development process
