Discovery of complex oxides via automated experiments and data science

Molecular Thin Films Enable the Synthesis and Screening of Nanoparticle Megalibraries Containing Millions of Catalysts

Megalibraries are centimeter-scale chips containing millions of materials synthesized in parallel using scanning probe lithography. As such, they stand to accelerate how materials are discovered for applications spanning catalysis, optics, and more. However, a long-standing challenge is the availability of substrates compatible with megalibrary synthesis, which limits the structural and functional design space that can be explored. To address this challenge, thermally removable polystyrene films were developed as universal substrate coatings that decouple lithography-enabled nanoparticle synthesis from the underlying substrate chemistry, thus providing consistent lithography parameters on diverse substrates. Multi-spray inking of the scanning probe arrays with polymer solutions containing metal salts allows patterning of >56 million nanoreactors designed to vary in composition and size. These are subsequently converted to inorganic nanoparticles via reductive thermal annealing, which also removes the polystyrene to deposit the megalibrary. Megalibraries with mono-, bi-, and trimetallic materials were synthesized, and nanoparticle size was controlled between 5 and 35 nm by modulating the lithography speed. Importantly, the polystyrene coating can be used on conventional substrates like Si/SiO_x, as well as substrates typically more difficult to pattern on, such as glassy carbon, diamond, TiO_2, BN, W, or SiC. Finally, high-throughput materials discovery is performed in the context of photocatalytic degradation of organic pollutants using Au-Pd-Cu nanoparticle megalibraries on TiO₂ substrates with 2,250,000 unique composition/size combinations. The megalibrary was screened within 1 h by developing fluorescent thin-film coatings on top of the megalibrary as proxies for catalytic turnover, revealing Au_0.53Pd_0.38Cu_0.09-TiO₂ as the most active photocatalyst composition.

Automated Reaction Kinetics of Gas-Phase Organic Species over Multiwell Potential Energy Surfaces

Automation of rate-coefficient calculations for gas-phase organic species became possible in recent years and has transformed how we explore these complicated systems computationally. Kinetics workflow tools bring rigor and speed and eliminate a large fraction of manual labor and related error sources. In this paper we give an overview of this quickly evolving field and illustrate, through five detailed examples, the capabilities of our own automated tool, KinBot. We bring examples from combustion and atmospheric chemistry of C-, H-, O-, and N-atom-containing species that are relevant to molecular weight growth and autoxidation processes. The examples shed light on the capabilities of automation and also highlight particular challenges associated with the various chemical systems that need to be addressed in future work.

Autonomous optimization of non-aqueous Li-ion battery electrolytes via robotic experimentation and machine learning coupling

Developing high-energy and efficient battery technologies is a crucial aspect of advancing the electrification of transportation and aviation. However, battery innovations can take years to deliver. In the case of non-aqueous battery electrolyte solutions, the many design variables in selecting multiple solvents, salts and their relative ratios make electrolyte optimization time-consuming and laborious. To overcome these issues, we propose in this work an experimental design that couples robotics (a custom-built automated experiment named "Clio") to machine-learning (a Bayesian optimization-based experiment planner named "Dragonfly"). An autonomous optimization of the electrolyte conductivity over a single-salt and ternary solvent design space identifies six fast-charging non-aqueous electrolyte solutions in two work-days and forty-two experiments. This result represents a six-fold time acceleration compared to a random search performed by the same automated experiment. To validate the practical use of these electrolytes, we tested them in a 220 mAh graphite∣∣LiNi_0.5Mn_0.3Co_0.2O₂ pouch cell configuration. All the pouch cells containing the robot-developed electrolytes demonstrate improved fast-charging capability against a baseline experiment that uses a non-aqueous electrolyte solution selected a priori from the design space.