Editorial: Shaping the future of materials science through machine learning

Abstract

This special issue of MGE advances focuses on the revolutionary impact of machine learning (ML) on materials science. As we navigate the threshold of a new era in scientific innovation, this issue collates a series of research articles that epitomize machine learning as a foundational pillar in materials science and engineering. The synergy between ML and conventional materials science methodologies not only accelerates the discovery of novel materials but also refines the prediction of material properties and streamlines manufacturing processes. These advances offer unparalleled opportunities for technological progress and sustainability. We, as the guest editors, are excited to present these contributions that introduce new methodologies and enhance our understanding of material behavior through the prism of advanced analytics and computational power.

This issue spans a diverse array of studies demonstrating the robust capabilities of ML applications across various scales and complexities within the field. Each article contributes to a broad exploration of how machine learning can be integrated into different facets of materials science. They range from quantum computing to enhancing materials design to predictive models that impact the properties and behavior of complex materials. The contributions showcase effective strategies to predict critical physical properties and illustrate the practical implementations of ML in optimizing the development processes of technological and industrial materials.

As we confront global challenges that demand more efficient, sustainable, and high performance materials, the research showcased here offers promising new pathways and tools. The integration of ML into materials science not only boosts our analytical capabilities but also accelerates the cycle of discovery and application, effectively bridging the gap between theoretical science and practical implementation.

The pages that follow represent articles at the forefront of this interdisciplinary nexus, providing insights expected to influence a broad spectrum of sectors, including electronics, aerospace, automotive, and beyond.

Dezhen Xue: Writing—review and editing. Turab Lookman: Writing—review and editing.

There is no conflict of interest.