  The emergent multi-principal element alloys (MPEAs) provide a vast
compositional space to search for novel materials for technological advances.
However, how to efficiently identify optimal compositions from such a large
design space for targeted properties is a grand challenge in material science.
Here we developed a convolutional neural network (CNN) model that can
accurately and efficiently predict path-dependent vacancy migration energy
barriers, which are critical to diffusion behaviors and many high-temperature
properties, of MPEAs at any compositions and with different chemical
short-range orders within a given alloy system. The success of the CNN model
makes it promising for developing a database of diffusion barriers for
different MPEA systems, which would accelerate alloy screening for the
discovery of new compositions with desirable properties. Besides, the length
scale of local configurations relevant to migration energy barriers is
uncovered, and the implications of this success to other aspects of materials
science are discussed.
