Explainable machine learning to enable high-throughput electrical
  conductivity optimization of doped conjugated polymers
Ji Wei Yoon Adithya Kumar Pawan Kumar Kedar Hippalgaonkar J Senthilnath Vijila Chellappan
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 first ML model
(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 second ML model (regression model), to
predict their conductivities, yielding an impressive test R2 value of 0.984. To
validate the approach, we showed that the models, neither trained on the
samples with the two highest conductivities of 498 and 506 S/cm, were able to,
in an extrapolative manner, correctly classify and predict them at satisfactory
levels of errors. The proposed ML 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.