Accelerating materials discovery: combinatorial synthesis, high-throughput characterization, and computational advances

Abstract

The acceleration of materials discovery has gained paramount importance due to its potential to overcome constraints in emerging technologies. Extensive exploration has been undertaken into three pivotal approaches: combinatorial synthesis, high-throughput characterization, and computational techniques, all aimed at unveiling new materials. This review article delves into recent progress in these domains. Combinatorial synthesis, especially in the development of thin-film materials libraries, emerges as a potent method for efficiently generating comprehensive multinary materials systems and composition gradients spanning the entire spectrum of required compositions. High-throughput characterization techniques play a pivotal role in assessing the compositional, structural, and functional attributes of materials within these libraries, yielding multidimensional datasets. Concurrently, recent advancements in computational materials science have notably expedited the discovery process by enabling high-throughput calculations and simulations of potential materials systems. These collective endeavors foster a more robust correlation between composition, processing, structure, and properties, facilitating the forecast and design of future materials through data-driven materials discovery. This approach allows for efficient optimization of newly identified materials. Furthermore, materials informatics, an integral element of this process, plays a crucial role in managing and extracting valuable insights from the vast data generated during materials discovery.

Keywords

• Combinatorial libraries
• high-throughput
• machine learning
• materials discovery