Abstract Machine learning is now applied in virtually every sphere of life for data analysis
and interpretation. The main strengths of the method lie in the relative ease of the …

Currently, the growth of material data from experiments and simulations is expanding
beyond processable amounts. This makes the development of new data‐driven methods for …

As the big data generated by the development of modern experiments and computing
technology becomes more and more accessible, the material design method based on …

The rapid advancement of big data and artificial intelligence has resulted in new data-driven
materials research and development (R&D), which has achieved substantial progress. This …

In material science, traditional experimental and computational approaches require
investing enormous time and resources, and the experimental conditions limit the …

Abstract Machine learning (ML) is shown to predict new alloys and their performances in a
high dimensional, multiple-target-property design space that considers chemistry, multi-step …

We are excited to present this Special Topic collection on Machine Learning for Materials
Design and Discovery in the Journal of Applied Physics. With a wide range of exciting and …

The history of machine learning (ML) can be traced back to the 1950 s, and its application in
alloy design has recently begun to flourish and expand rapidly. The driving force behind this …

Interatomic potentials are essential for studying fundamental mechanisms of deformation
and failure in metals and alloys because the relevant defects (dislocations, cracks, etc.) are …

The development of new materials, incorporation of new functionalities, and even the
description of well-studied materials strongly depends on the capability of individuals to …