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

arXiv:2308.04103v2 Announce Type: replace-cross
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 …

abstract arxiv automate combination cond-mat.mtrl-sci cs.lg discovery edge enabling experimentation explainable machine learning however identification machine machine learning material materials measurement optimization physics.app-ph type