ADVANCED MATERIALS & PROCESSES | MAY/JUNE 2024 18 gathering of literature data, and conducting simulations dynamically to fill the gap of missing information. This integration had significant progress in the fields whose high-throughput experiments can be performed fast and relatively easy with robots. One important example is the long history of automated drug discovery in pharmaceutical research[11,12]. The more complex nature of experiments in materials research hindered adoption of this approach, and currently about two decades are required before a material discovery reaches a market. The initial case studies in materials research were mainly on less time-consuming experiments in chemistry or energy applications[13-15]. However, there is little progress for fields like structural alloy design for enhanced mechanical properties, for which each experiment and characterization is extremely long. The rapid emergence of additive manufacturing enables easier automation of the alloy processing. It is hoped that gathering insights from the successful stories and exploiting additive manufacturing leads to adoption and thus acceleration and novelties in these more challenging cases. In the following, two recent showcases are shared that present promising routes for efficient materials discoveries: Case Study 1. Szymanski et al. developed an autonomous laboratory for the solid-state synthesis of inorganic powders[16]. See Fig. 5. They used phase stability data from large scale ab initio Fig. 4 — Active learning search for high-performance organic semiconductors. Reprinted with permission from Ref. 9. Copyright 2020 American Chemical Society. Fig. 5 — Autonomous materials discovery with the A-Lab. Reprinted with permission from Ref. 16 under the terms of the Creative Commons CC BY license.
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