Multi‐Fidelity High‐Throughput Optimization of Electrical Conductivity in P3HT‐CNT Composites

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.

Machine Learning Predicts Conversion and Molecular Weight Distributions in Computer Controlled Polymerization

“…ML methods have increasingly been incorporated into recent materials discovery research efforts 8,9 , and range in its myriad applications from organic synthesis [10][11][12][13][14] , to drug discovery [15][16][17] , polymer design [18][19][20][21][22] and many other areas [23][24][25][26][27] , owing to their ability to dramatically speed up scientific discovery 28 and guide scientific experiments. 29 Coupled with the proliferation of ML in scientific research, computercontrolled platforms and automated workflows have also become increasingly prominent in scientific research.…”

High‐throughput and High‐speed Absorbance Measurements in Microfluidic Droplets using Hyperspectral Imaging

“…The interest in high‐throughput experimental platforms performing both material synthesis and fast inline material characterization has considerably grown over the last decade due to the integration of machine learning in materials science [1] . High‐throughput experimental platforms have already shown enhanced screening efficiency in the discovery of active pharmaceutical ingredients, [2] catalysts, [3] perovskite materials for photovoltaics, [4] thermoelectric materials [5] or polymers [6] . To accelerate the screening of the parameter space, different techniques are generally considered, some based on the use of microarrays, others on flow synthesis.…”

Extracting film thickness and optical constants from spectrophotometric data by evolutionary optimization

“…Computationally, it is not trivial to achieve accurate estimates of all the optical parameters together [ 11 , 12 ], since the inverse problem of retrieving the optical characteristics of a film from spectral information is highly non-linear and often ill-conditioned. Accurate measurement of thickness using experimental methods is a time-consuming process in practice [ 13 , 14 ]. Usually, the most accurate thickness is attained while compromising accuracy of the estimated refractive index [ 15 , 16 ].…”