Machine learning and high-throughput robust design of P3HT-CNT composite thin films for high electrical conductivity

TL;DR

This paper presents a machine learning-driven high-throughput experimental approach for optimizing and understanding the electrical conductivity of P3HT-CNT composite thin films, enabling rapid property mapping and scientific insights.

Contribution

It introduces an automated high-throughput system combined with machine learning for rapid optimization and scientific understanding of composite thin films.

Findings

Achieved electrical conductivities up to 1200 S/cm in P3HT-CNT films.

Discovered a non-intuitive local optimum with 10% double-walled CNTs and long SWCNTs.

Linked charge delocalization to electrical conductivity through optical characterization.

Abstract

Combining high-throughput experiments with machine learning allows quick optimization of parameter spaces towards achieving target properties. In this study, we demonstrate that machine learning, combined with multi-labeled datasets, can additionally be used for scientific understanding and hypothesis testing. We introduce an automated flow system with high-throughput drop-casting for thin film preparation, followed by fast characterization of optical and electrical properties, with the capability to complete one cycle of learning of fully labeled ~160 samples in a single day. We combine regio-regular poly-3-hexylthiophene with various carbon nanotubes to achieve electrical conductivities as high as 1200 S/cm. Interestingly, a non-intuitive local optimum emerges when 10% of double-walled carbon nanotubes are added with long single wall carbon nanotubes, where the conductivity is seen to be as high as 700 S/cm, which we subsequently explain with high fidelity optical characterization. Employing dataset resampling strategies and graph-based regressions allows us to account for experimental cost and uncertainty estimation of correlated multi-outputs, and supports the proving of the hypothesis linking charge delocalization to electrical conductivity. We therefore present a robust machine-learning driven high-throughput experimental scheme that can be applied to optimize and understand properties of composites, or hybrid organic-inorganic materials.