Workshop: AI for Accelerated Materials Design (AI4Mat)

A self-driving laboratory optimizes a scalable materials manufacturing process

Connor Rupnow · Benjamin MacLeod · Mehrdad Mokhtari · Karry Ocean · Kevan Dettelbach · Daniel Lin · Fraser Parlane · Hsi Chiu · Michael Rooney · Christopher Waizenegger · Elija de Hoog · Curtis Berlinguette

Keywords: [ Bayesian optimization ] [ Self-driving laboratory ] [ spray-coating ] [ thin films and coatings ] [ autonomous experiments ]


Solution-based coating methods offer a low-cost method for depositing coatings at scale. It is difficult, however, to obtain high quality coatings using these methods due to the complex physical phenomena at play. Here, we show how a self-driving laboratory can optimize a scalable spray-coating process applicable to manufacturing diverse clean-energy technologies. We demonstrate this system by optimizing a spray-combustion process for depositing conductive palladium films. This optimization yielded films with conductivities of 4.08 MS/m, doubling the state-of-the art film conductivity possible with this process and rivaling Pd film conductivities obtained using vacuum-based sputtering processes (2 to 6 MS/m). The rich data gathered by the self-driving laboratory also provides mechanistic insights into the coating process. The champion coating conditions were scaled up to an 8× larger area using the same spray-coating apparatus with no further optimization and no reduction in coating quality. This work shows how self-driving laboratories can optimize spray-coating for depositing coatings at scale.

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