Autonomous materials research and design: Characterization
Source abstract
Abstract
• This work provides an overview of the technical and conceptual components related to materials characterization discussed during a workshop dedicated to challenging the way materials research is undertaken. • Within the emergent field of autonomous materials research and design (AMRAD), new materials development has accelerating and enabling new technologies and capabilities. • General considerations for developing autonomous characterization are presented along with related works and a discussion of their progress and shortcomings toward the AMRAD vision. New materials are a fundamental component of most major advancements in human history. The pivotal role materials play in the development of next generation technologies has spurred campaigns such as the Materials Genome Initiative (MGI) with the goal of reducing the time and cost to discover, characterize, and deploy advanced materials. As goals of the MGI have been met and new capabilities have emerged, a contemporary vision has taken shape within the scientific community whereby the exploration of materials space is dramatically accelerated by artificial intelligence agent(s) capable of performing research independently from humans and achieving a paradigm change in the field. As this idea comes to fruition and new materials are more rapidly computationally evaluated and synthesized nearly on demand, the rate at which a complete characterization of each candidate material’s properties can be completed and understood within the context of all other potential solutions will be the next bottleneck in a materials design campaign. This work provides an overview of the technical and conceptual components related to materials characterization discussed during a workshop dedicated to challenging the way materials research is thought of and performed within the emergent field of autonomous materials research and design (AMRAD). Furthermore, general considerations for developing autonomous characterization are presented along with related works and a discussion of their progress and shortcomings toward the AMRAD vision.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Machine Learning in Materials Science · Additive Manufacturing and 3D Printing Technologies · Manufacturing Process and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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