Materials Scientists
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Research and study the structures and chemical properties of various natural and synthetic or composite materials, including metals, alloys, rubber, ceramics, semiconductors, polymers, and glass. Determine ways to strengthen or combine materials or develop new materials with new or specific properties for use in a variety of products and applications. Includes glass scientists, ceramic scientists, metallurgical scientists, and polymer scientists.
The occupation of "Materials Scientists" carries an automation risk of 49.1%, which is slightly below the base risk of 50.0%. This risk assessment reflects the dual nature of the role: while many technical and repetitive tasks can be automated, significant elements of the job still require complex human judgement and creativity. The work often revolves around the development, testing, and analysis of materials such as metals, alloys, polymers, and ceramics. With strong advances in AI, machine learning, and laboratory robotics, the technical facets of the position have become increasingly susceptible to automation, as algorithms and automated equipment can efficiently perform many testing and data analysis functions with high accuracy. However, complete automation is hindered by tasks that demand human insight and innovation. The three most automatable tasks in this profession include: conducting research on the structures and properties of materials to support new product development or enhancement, testing metals to determine their mechanical and physical property conformance, and testing material samples under varied stresses to identify the causes of failure. These activities typically involve structured processes, large datasets, and repeatable procedures—making them ideal candidates for automation technologies. Advances in laboratory automation now allow machines to conduct, monitor, and record experiments at a scale and speed far greater than humans, reducing human involvement in routine testing and analysis. Machine learning algorithms further support these processes by identifying trends, patterns, or anomalies in complex datasets, thus enabling rapid materials innovation. Conversely, three tasks remain highly resistant to automation: teaching in colleges and universities, writing research papers for scientific publication, and visiting suppliers or users to gather detailed information. These responsibilities require advanced interpersonal skills, adaptive communication, critical thinking, and a high degree of originality—attributes that current AI and automation systems lack. Bottleneck skills such as originality, with scores of 3.3% and 4.1%, indicate the importance of creative thinking for tasks like scientific publication and developing novel materials. While automation can aid in data collection and preliminary analysis, the interpretive and innovative elements of materials science rely on human expertise and intuition, preserving a significant human component within the occupation.