Carnegie Mellon

Assembly Line

Closed-loop fully-automated frameworks for accelerating materials discovery

📅 Date:

🔖 Topics: Machine Learning, Materials Science

🏢 Organizations: Citrine Informatics, Carnegie Mellon, MIT

Our work shows that a fully-automated closed-loop framework driven by sequential learning can accelerate the discovery of materials by up to 10-25x (or a reduction in design time by 90-95%) when compared to traditional approaches. We show that such closed-loop frameworks can lead to enormous improvement in researcher productivity in addition to reducing overall project costs. Overall, these findings present a clear value proposition for investing in closed-loop frameworks and sequential learning in materials discovery and design enterprises.

Read more at Citrine Informatics Blog