Materials Scientists

With an automation risk score of 32%, Materials Scientists has a low risk of AI replacement. Most tasks in this role require skills that are difficult for AI to replicate, such as complex decision-making, physical dexterity, or deep interpersonal interaction. AI is more likely to serve as a supportive tool.

The AI automation risk score for Materials Scientists is 32% (2025 data). Overall AI exposure is 44%, with 62% theoretical exposure and 28% observed exposure. The risk trend from 2023 to 2025 is +12 points.

The tasks with the highest automation potential for Materials Scientists are: Simulate material properties using computational models (68%), Review scientific literature and synthesize prior research (60%), Analyze experimental data and publish research findings (52%). These rates reflect how much of each task current AI systems can handle, based on research data from Anthropic and academic sources.

The BLS projects +6% employment change for Materials Scientists from 2024 to 2034. Combined with an overall AI exposure of 44%, this occupation is experiencing both traditional labor market shifts and AI-driven transformation. Workers should monitor both employment trends and AI capability growth.

Since AI primarily augments capabilities in this role, professionals in Materials Scientists should embrace AI as a productivity multiplier. Focus on learning to use AI tools effectively, developing higher-order analytical and creative skills, and positioning yourself as someone who can leverage AI to deliver greater value.