Accelerated automated screening of viscous graphene suspensions for optimal electrical conductivity

January 2022: Digital Discovery

https://pubs.rsc.org/en/content/articlehtml/2022/dd/d1dd00008j

Functional composite thin films have a wide variety of applications in flexible and/or electronic devices, telecommunications and multifunctional emerging coatings. Rapid screening of their properties is a challenging task, especially with multiple components defining the targeted properties. In this work we present a manifold for accelerated automated screening of viscous graphene suspensions for optimal electrical conductivity. Using Opentrons OT2 robotic auto-pipettor, we tested 3 most industrially significant surfactants - PVP, SDS and T80 - by fabricating 288 samples of graphene suspensions in aqueous hydroxypropylmethylcellulose. Enabled by our custom motorized 4-point probe measurement setup and computer vision algorithms, we then measured electrical conductivity of every sample and identified that the highest performance is achieved for PVP-based samples, peaking at 10.4 mS/cm. The automation of the experimental procedure allowed us to perform the majority of the experiments using robots, while involvement of human researchers was kept to minimum. Overall the experiment was completed in less than 18 hours, only 3 of which involved humans.

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

June 2021: Advanced Functional Materials

https://onlinelibrary.wiley.com/doi/abs/10.1002/adfm.202102606

Combining high-throughput experiments with machine learning accelerates materials and process optimization toward user-specified target properties. In this study, we developed a high-throughput, machine learning-driven mixing and drop-casting system to fabricate the films, as well as tools and procedures for rapid proxy measurements of electrical and optical properties of these films. In the end we were able to make 160 composites of poly-3-hexylthiophene and various types of nanotubes per day, which is more than 10x improvement compared to baseline. Graph-based model selection strategies with classical regression, in combination with Bayesian Optimization, was used to suggest next experiments to try and predict their performance. In 12 cycles of experiments, the ML predictions and measured results converged, yielding the optimal composition of a target material, with state-of-the-art conductivity of 1000 S/cm. These results were later verified with manual high-fidelity experiments. 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.

An Object-Oriented Framework to Enable Workflow Evolution across MAPs

October 2022: Matter

https://doi.org/10.1016/j.matt.2022.08.017

Materials Acceleration Platforms (MAPs) autonomously perform AI-guided, high-throughput experiments in the pursuit of materials discovery. Currently, MAPs are developed and operated in isolation by research groups, limiting their utility to their specific domains and groups. Furthermore, since developing a MAP is a time- and resource-intensive process, engineering constraints to their capabilities cannot be avoided. Surpassing these limitations require next-generation MAPs to work in collaboration among research groups, and the adoption of a common framework to plan experimental workflows could achieve this. Herein, we propose a framework to produce evolving experimental workflows that facilitate the interfacing between MAPs and enable collaborators to share knowledge, data, and tools. Through the wide adoption of our framework, we envision a global community of MAPs working together to solve the scientific grand challenges required to solve pressing societal problems.