Explainable machine learning to enable high-throughput electrical conductivity optimization and discovery of doped conjugated polymers

TL;DR

This paper presents an explainable machine learning approach that uses absorbance spectra to rapidly classify and predict electrical conductivity in doped conjugated polymers, significantly improving measurement efficiency and providing insights into spectral influences.

Contribution

It introduces an ML workflow that classifies and predicts high conductivity in doped polymers using spectral data, with enhanced explainability and 89% efficiency improvement.

Findings

Achieved 100% accuracy in classifying highly conductive samples

Predicted conductivities with R2 of 0.984 for regression models

Improved measurement efficiency by 89%

Abstract

The combination of high-throughput experimentation techniques and machine learning (ML) has recently ushered in a new era of accelerated material discovery, enabling the identification of materials with cutting-edge properties. However, the measurement of certain physical quantities remains challenging to automate. Specifically, meticulous process control, experimentation and laborious measurements are required to achieve optimal electrical conductivity in doped polymer materials. We propose a ML approach, which relies on readily measured absorbance spectra, to accelerate the workflow associated with measuring electrical conductivity. The classification model accurately classifies samples with a conductivity > 25 to 100 S/cm, achieving a maximum of 100 % accuracy rate. For the subset of highly conductive samples, we employed a regression model to predict their conductivities, yielding an impressive test R2 value of 0.984. We tested the models with samples of the two highest conductivities (498 and 506 S/cm) and showed that they were able to correctly classify and predict the two extrapolative conductivities at satisfactory levels of errors. The proposed ML-assisted workflow results in an improvement in the efficiency of the conductivity measurements by 89 % of the maximum achievable using our experimental techniques. Furthermore, our approach addressed the common challenge of the lack of explainability in ML models by exploiting bespoke mathematical properties of the descriptors and ML model, allowing us to gain corroborated insights into the spectral influences on conductivity. Through this study, we offer an accelerated pathway for optimizing the properties of doped polymer materials while showcasing the valuable insights that can be derived from purposeful utilization of ML in experimental science.