Materials Scientist Materials Scientists
職業コード: 19-2032（SOC） 技能移住対象職業 総合 7.2/10
Materials scientists study the structure and properties of natural and synthetic materials, developing new materials for applications in electronics, aerospace, medical devices, and more.
評価 · 総合 7.2/10i
In the AI era: what happens to Materials Scientist
AI's impact on materials scientists is mixed: data analysis and simulation predictions will be automated, but experimental design, interdisciplinary innovation, and physical intuition remain core human strengths.
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Replaces part of the work of materials scientists in experimental screening and computational prediction of new materials, significantly shortening the material development cycle.
↗ データソース -
Replaces much of the work of materials scientists in discovering new crystal materials through experimental synthesis and characterization, enabling high-throughput virtual screening.
↗ データソース -
Partially replaces material scientists' repetitive tasks in experimental data management, property prediction, and formula optimization.
↗ データソース -
Partially replaces material scientists' work using first-principles calculations for electronic structure analysis, offering efficient alternatives.
↗ データソース - Alexandr Wang's material prediction tools Research Partial 2023
Partially replacing material scientists' work in mechanical property testing and simulation modeling, accelerating material screening.
↗ データソース -
Partially replaces materials scientists in information processing tasks such as literature research, data collation, and experiment planning.
↗ データソース
- Automatic search and matching of material property databases
- Performance prediction based on known data (e.g., density functional theory calculations)
- Automatic generation of lab reports and anomaly detection
- Literature review and knowledge graph construction
- Automated execution of standard repetitive tests (e.g., tensile, hardness testing)
- AI accelerates materials discovery: generative models design new alloy/polymer candidates
- Machine learning optimizes experimental parameters (e.g., temperature, pressure, ratio)
- Self-driving labs assist high-throughput screening
- Natural language processing tools assist cross-field literature interpretation and patent analysis
- Computer vision analysis of microstructure images (SEM/TEM)
- Establish new theoretical models to explain anomalous experimental phenomena
- Creatively combining material properties with end-use application needs (e.g., biodegradable medical devices)
- Processing unstructured, small-sample, or noisy experimental data
- Communication and systems thinking in interdisciplinary collaboration (with biology, electronics, mechanical engineering)
- Python/R data science and machine learning (scikit-learn, PyTorch)
- Density functional theory (DFT) and molecular dynamics simulations (VASP, LAMMPS)
- Lab automation and robotic operation (e.g., Chemputer platform)
- Materials informatics and databases (Materials Project)
- Generative AI (e.g., GPT-4 for experiment design, generative models for new material prediction)
Entry-level roles like materials testing technician, basic data analyst may decrease as AI can handle routine characterization and literature screening. But demand rises for high-skilled research assistants with AI tool proficiency.
Materials scientists should shift to an 'AI+experiment' hybrid role: master machine learning-driven inverse materials design, leverage automated labs for faster iteration, and delve deep into the physico-chemical mechanisms of a specific application area (e.g., batteries, semiconductors), thus upgrading from 'trial-and-error' to 'intelligent designer' and leading innovation directions.
給与
| 経験 | 年収 (USD) | |
|---|---|---|
| 初級（0～3年） | $65,000 ~ $85,000 | Postdoctoral or entry-level R&D scientist |
| Intermediate (3-10 years) | $85,000 ~ $120,000 | Research Scientist/Project Manager |
| Senior (10+ years) | $120,000 ~ $160,000 | Chief Scientist/R&D Director |
教育パス
| 段階 | 期間 | 費用 (USD) |
|---|---|---|
| Bachelor's degree | 4年 | $40,000~$150,000 |
| Doctorate | 5-6 years. | $0~$0 |
資格
| 資格 | 発行機関 | |
|---|---|---|
| PhD in materials science or related field | University | 必須 |
| Engineer license (optional) | State engineering board | 任意 |
移住
Occupation classification code: 19-2032（SOC）
| ビザ | 詳細 |
|---|---|
| H-1B H-1B Specialty Occupation Visa | Most common work visa, requires bachelor's degree or higher, with quota limits and lottery |
| EB-2 Employment-Based Second Preference (EB-2) | Requires master's degree or above, or bachelor's + 5 years experience, typically needing PERM labor certification |
| O-1 O-1 Extraordinary Ability Visa | Applicable to individuals with extraordinary ability, no quota limits, must demonstrate exceptional competence |
向いている人
- People curious about material microstructure and properties.
- Those who enjoy laboratory research and interdisciplinary collaboration
- People willing to pursue a PhD and engage in R&D work
- Those who dislike long hours of experimentation and data analysis
- Those unwilling to undergo years of academic training
キャリア見通し
Career progression path: Research Scientist → Senior Researcher → Chief Scientist/R&D Director; or transition to project management, technical consulting.
U.S. materials scientist employment projected to grow 7% (2022-2032), driven by advanced manufacturing, nanotechnology, and renewable energy R&D.
成長分野：
Advanced materialsNanotechnologyRenewable energyBiomaterials
FAQ
データソース
Salary ranges are estimates aggregated from public listings on Indeed, Glassdoor, ERI SalaryExpert and the U.S. Bureau of Labor Statistics (BLS OEWS); employment and demand outlook cite the BLS Occupational Outlook and O*NET; visa and migration details follow the latest USCIS work-visa (H-1B / O-1 / L-1) and employment-based green-card (EB-2 / EB-3, incl. DOL PERM labor certification) rules. Figures are indicative only — always refer to the latest official sources.