Self-Driving Laboratories: Translating Materials Science from Laboratory to Factory
Abstrak
The field of materials science stands at a critical inflection point. While laboratory innovations continue to emerge at an unprecedented pace, the traditional timeline from discovery to market in 10–20 years has become an unacceptable bottleneck in addressing urgent technological challenges. We argue that self-driving laboratories (SDLs) represent not merely another step in automation, but a fundamental reimagining of the materials development pipeline. By integrating manufacturing constraints and scalability considerations from the earliest stages of discovery, SDLs can collapse the laboratory-to-factory timeline while improving reproducibility and success rates. This requires abandoning the traditional sequential approach of materials screening, device optimization and manufacturing scale-up; in favor of concurrent cross-scale development. Here, we critically examine current SDL implementations, challenge prevailing assumptions about automation in materials science, and propose a roadmap for truly integrated materials development platforms that could revolutionize how we translate laboratory discoveries into commercial products.
Topik & Kata Kunci
Penulis (4)
Andre K. Y. Low
J. J. W. Cheng
K. Hippalgaonkar
L. W. Ng
Akses Cepat
- Tahun Terbit
- 2025
- Bahasa
- en
- Total Sitasi
- 5×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1021/acsomega.5c02197
- Akses
- Open Access ✓