Somewhat Resilient

Last Update: 8/30/2026

AI Resilience Score for Materials Scientists:

47.2%

Median Score

Meaningful human contribution

Med

Long-term employer demand

Med

Sustained economic opportunity

Med

Our confidence in this score:
Medium

Contributing sources

Methodology and Scoring Rationale

To score how resilient materials science is to AI, we ask one question in three parts:

First, how much of the job still needs a human, read from five AI-exposure sources: our own AI Resilience Model, Anthropic's Observed Exposure, Microsoft's AI Applicability, Will Robots Take My Job, and OpenAI Signals. We call this dimension Meaningful Human Contribution (MHC) and weight it at 40%.

Next, whether employers will keep hiring for this job over the long term. This dimension, which we call Long-term Employer Demand (LTE), is calculated from BLS data and weighted at 30%.

Last, whether pay and mobility will hold up. We use wage bill and adaptive capacity data from independent researchers (Althoff & Reichardt, 2026; Manning & Aguirre, 2026). We call this dimension Sustained Economic Opportunity (SEO) and weight it at 30%.

For materials scientists, all eight sources had data, though AI exposure was split: Will Robots Take My Job saw strong human involvement, while AI Resilience Model and Microsoft flagged meaningful AI overlap, keeping confidence at medium. Employer demand and pay are both moderate, with Adaptive Capacity as a bright spot, landing this career at "Somewhat Resilient."

AI Resilience Report forMaterials Scientists

$117,790 median salary600 annual openingsSOC Code: 19-2032.00

Materials Scientists are somewhat less resilient to AI impacts than most occupations, according to our analysis of 8 sources.

Materials science is labeled "Somewhat Resilient" because AI is genuinely changing how a big chunk of the work gets done, especially the research and analysis side, where tools can now run simulations, scan thousands of papers, and even suggest new ideas in days instead of months. That means the job is shifting: scientists who once spent most of their time on repetitive data crunching will need to focus more on guiding AI tools, asking the right questions, and making creative judgment calls that software cannot make on its own.

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This role is somewhat resilient

Materials science is labeled "Somewhat Resilient" because AI is genuinely changing how a big chunk of the work gets done, especially the research and analysis side, where tools can now run simulations, scan thousands of papers, and even suggest new ideas in days instead of months. That means the job is shifting: scientists who once spent most of their time on repetitive data crunching will need to focus more on guiding AI tools, asking the right questions, and making creative judgment calls that software cannot make on its own.

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Analysis of Current AI Resilience

Materials Scientists

Updated Quarterly

Analysis
Suggested Actions
State of Automation

How is AI changing Materials Scientists jobs?

Right now, AI is mostly augmenting materials scientists — helping them work faster — rather than replacing them. At Argonne National Laboratory, researchers built a team of AI agents that automates atomistic simulations from start to finish, potentially reducing discovery time from months or years to just days, using collaborating specialist agents [1] that pick models, run simulations, and analyze results after a scientist types a plain-English prompt. For writing and reading papers, a team at IIT Delhi trained LLaMat, a materials-specific language model [2], which was evaluated across 42 tasks including natural-language understanding, structured information extraction, and crystal structure generation, and outperformed several widely used commercial LLMs like Claude, GPT and Gemini.

Researchers at Karlsruhe Institute of Technology are also using AI to scan mountains of journal articles for new research ideas [3], but the lead scientist stressed the findings aren't an invention machine — they're an analytic tool to identify new ideas and provide targeted support for scientific creativity. Physical lab visits, supervising production, and judgment calls about safety remain firmly human.

Sources

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AI Adoption

How fast is AI adoption growing for Materials Scientists?

Adoption is moving quickly on the research side because commercial tools are cheap while lab experiments are slow and expensive — Deloitte's 2026 manufacturing outlook [4] highlights AI and agentic systems as top competitive priorities, and the field's leading society, TMS, just hosted the 4th World Congress on AI in Materials and Manufacturing (AIM 2026) [5] to help members apply these tools. Adoption is slower for tasks that need hands-on lab work, supplier visits, or safety accountability. Encouragingly, the U.S. Bureau of Labor Statistics projects [6] that employment of chemists and materials scientists will grow 7% from 2025 to 2035 — much faster than average — suggesting AI is expanding, not shrinking, opportunities for curious young scientists.

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Will AI replace Materials Scientists?

Will AI replace Materials Scientists?

Not entirely. We think AI will take over some tasks, but not the whole job.

Materials science sits at a 47.2% AI Resilience Score, which tells you the field is genuinely changing. AI is already doing real work here. At Argonne National Laboratory, AI agents now automate atomistic simulations end to end, compressing what once took months into days [1]. Specialized models can scan thousands of journal articles to surface new research ideas, though researchers are clear these tools support scientific creativity rather than replace it [3]. Routine literature review, data extraction, and simulation setup are all moving toward AI assistance fast.

What stays human is the harder stuff: physical lab work, safety accountability, supplier relationships, and the judgment calls that come with turning a promising simulation into a real material. Those aren't going away. Adoption of AI tools is also uneven, moving quickly on the research side but slowly wherever hands-on work is required [4].

The job market picture is cautiously encouraging. The U.S. Bureau of Labor Statistics projects 7% employment growth for chemists and materials scientists through 2035, faster than average, which suggests AI is opening doors rather than closing them [6]. The scientists who learn to work alongside these tools will likely be the most valuable ones in the room.

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Latest AI news for Materials Scientists

These articles highlight the transformative impact of AI on materials science careers. For instance, the Materials Project is providing vast AI-ready datasets, enabling researchers to accelerate discoveries. Additionally, educational initiatives at institutions like the University of Wisconsin focus on integrating AI into curricula, preparing students for this evolving field. As companies like Radical AI establish advanced labs, new job opportunities will emerge, emphasizing the need for professionals skilled in both materials science and AI. Embracing these changes can enhance career resilience in a rapidly advancing industry.

More Career Info

Career: Materials Scientists

They study different materials to understand how they work and create new ones for products like phones, cars, and sports gear.

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Employment & Wage Data

Median Wage

$117,790

Jobs (2025)

8,600

Growth (2025-35)

+8.3%

Annual Openings

600

Education

Bachelor's degree

Experience

None

Source: Bureau of Labor Statistics, Employment Projections 2025-2035

Task-Level AI Resilience Scores

AI-generated estimates of task resilience over the next 3 years

1

92% ResilienceSupplemental

Teach in colleges and universities.

2

88% ResilienceCore Task

Visit suppliers of materials or users of products to gather specific information.

3

82% ResilienceCore Task

Plan laboratory experiments to confirm feasibility of processes and techniques used in the production of materials with special characteristics.

4

80% ResilienceCore Task

Supervise and monitor production processes to ensure efficient use of equipment, timely changes to specifications, and project completion within time frame and budget.

5

78% ResilienceCore Task

Confer with customers to determine how to tailor materials to their needs.

6

78% ResilienceCore Task

Research methods of processing, forming, and firing materials to develop such products as ceramic dental fillings, unbreakable dinner plates, and telescope lenses.

7

75% ResilienceCore Task

Conduct research on the structures and properties of materials, such as metals, alloys, polymers, and ceramics, to obtain information that could be used to develop new products or enhance existing one...

Tasks are ranked by their AI resilience, with the most resilient tasks shown first. Core tasks are essential functions of this occupation, while supplemental tasks provide additional context.

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