Science · SOC 19-2032
AI accelerates materials discovery and literature review, but experimental validation and novel material design stay human-led.
Parts of the job are exposed, but judgment and context still matter.
Timeline: Generative AI for materials discovery (e.g., GNoME-style tools) will reshape literature review and candidate screening by 2027, but experimental validation remains hands-on.
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$93,330 median pay
$166,290 median pay
$109,840 median pay
$100,340 median pay
$93,330 median pay
$100,590 median pay
AI job-risk card
Materials Scientists is watch on AIFear. Parts of the job are exposed, but judgment and context still matter.
AIFear.com
Our AI Fear Score for Materials Scientists is 50/100 (Watch). Parts of the job are exposed, but judgment and context still matter.
50 out of 100, based on an AI-model review of this occupation's O*NET tasks against what current AI can actually do.
Most exposed: Writing research papers and technical reports, Planning experiments based on literature synthesis, Developing and validating testing methodologies, Analyzing structural and property data. Harder to automate: Teaching and mentoring university students, Running physical experiments with new materials, Interpreting unexpected experimental findings, Translating research into manufacturable products.
Lean into AI-driven materials informatics, High-throughput experimental design, Materials characterization techniques, Intellectual property and commercialization. Consider roles such as Materials science faculty or R&D manager in advanced manufacturing or Battery or semiconductor materials specialist.
Tasks, skills and AI-exposure for this role were reviewed by an AI model against its O*NET task profile (curated roles use published research anchors). Pay and employment, where shown, are U.S. BLS OEWS (May 2024) figures. This is an estimate, not a prediction about any individual's job. Compare all occupations.