| Affiliation: | 1. Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), #08-03, 2 Fusionopolis Way, Innovis, Singapore, 138634 Singapore;2. National University of Singapore, 3 Science Drive 3, Singapore, 117543 Singapore;3. National University of Singapore, 3 Science Drive 3, Singapore, 117543 Singapore
Singapore-MIT Alliance for Research and Technology, 1 Create Way, #10-01 & #09-03 CREATE Tower, Singapore, 138602 Singapore;4. Singapore-MIT Alliance for Research and Technology, 1 Create Way, #10-01 & #09-03 CREATE Tower, Singapore, 138602 Singapore |
| Abstract: | Combining high-throughput experiments with machine learning accelerates materials and process optimization toward user-specified target properties. In this study, a rapid machine learning-driven automated flow mixing setup with a high-throughput drop-casting system is introduced for thin film preparation, followed by fast characterization of proxy optical and target electrical properties that completes one cycle of learning with 160 unique samples in a single day, a > 10 × improvement relative to quantified, manual-controlled baseline. Regio-regular poly-3-hexylthiophene is combined with various types of carbon nanotubes, to identify the optimum composition and synthesis conditions to realize electrical conductivities as high as state-of-the-art 1000 S cm−1. The results are subsequently verified and explained using offline high-fidelity experiments. Graph-based model selection strategies with classical regression that optimize among multi-fidelity noisy input-output measurements are introduced. These strategies present a robust machine-learning driven high-throughput experimental scheme that can be effectively applied to understand, optimize, and design new materials and composites. |
