Discovering and optimizing commercially viable materials for clean energy applications typically takes more than a decade. Self-driving laboratories that iteratively design, execute, and learn from materials science experiments in a fully autonomous loop present an opportunity to accelerate this research process. We report here a modular robotic platform driven by a model-based optimization algorithm capable of autonomously optimizing the optical and electronic properties of thin-film materials by modifying the film composition and processing conditions. We demonstrate the power of this platform by using it to maximize the hole mobility of organic hole transport materials commonly used in perovskite solar cells and consumer electronics. This demonstration highlights the possibilities of using autonomous laboratories to discover organic and inorganic materials relevant to materials sciences and clean energy technologies.

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Useful materials must satisfy multiple objectives, where the optimization of one objective is often at the expense of another. The Pareto front reports the optimal trade-offs between these conflicting objectives. Here we use a self-driving laboratory, Ada, to define the Pareto front of conductivities and processing temperatures for palladium films formed by combustion synthesis. Ada discovers new synthesis conditions that yield metallic films at lower processing temperatures (below 200 °C) relative to the prior art for this technique (250 °C). This temperature difference makes possible the coating of different commodity plastic materials (e.g., Nafion, polyethersulfone). These combustion synthesis conditions enable us to to spray coat uniform palladium films with moderate conductivity (1.1 × 105 S m−1) at 191 °C. Spray coating at 226 °C yields films with conductivities (2.0 × 106 S m−1) comparable to those of sputtered films (2.0 to 5.8 × 106 S m−1). This work shows how a self-driving laboratoy can discover materials that provide optimal trade-offs between conflicting objectives.

Electrocatalytic palladium membrane reactors (ePMRs) use electricity and water to drive hydrogenation reactions without forming H2 gas.

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Opium poppy (Papaver somniferum) produces a large number of benzylisoquinoline alkaloids, including the narcotic analgesics morphine and codeine, and has emerged as one of the most versatile model systems to study alkaloid metabolism in plants. As summarized in this review, we have taken a holistic strategy-involving biochemical, cellular, molecular genetic, genomic, and metabolomic approaches-to draft a blueprint of the fundamental biological platforms required for an opium poppy cell to function as an alkaloid factory. The capacity to synthesize and store alkaloids requires the cooperation of three phloem cell types-companion cells, sieve elements, and laticifers-in the plant, but also occurs in dedifferentiated cell cultures. We have assembled an opium poppy expressed sequence tag (EST) database based on the attempted sequencing of more than 30,000 cDNAs from elicitor-treated cell culture, stem, and root libraries. Approximately 23,000 of the elicitor-induced cell culture and stem ESTs are represented on a DNA microarray, which has been used to examine changes in transcript profile in cultured cells in response to elicitor treatment, and in plants with different alkaloid profiles. Fourier transform-ion cyclotron resonance mass spectrometry and proton nuclear magnetic resonance mass spectroscopy are being used to detect corresponding differences in metabolite profiles. Several new genes involved in the biosynthesis and regulation of alkaloid pathways in opium poppy have been identified using genomic tools. A biological blueprint for alkaloid production coupled with the emergence of reliable transformation protocols has created an unprecedented opportunity to alter the chemical profile of the world's most valuable medicinal plant.