Novel materials are the backbone of major technological advances. However, the development and wide-scale introduction of new materials, such as nanomaterials, is limited by three main factors—the expense of experiments, inefficiency of synthesis methods and complexity of scale-up. Reaching the kilogram scale is a hurdle that takes years of effort for many nanomaterials. We introduce an improved methodology for materials development, combining state-of-the-art techniques—multi-objective machine learning optimization, high yield microreactors and high throughput analysis. We demonstrate this approach through the optimization of ZnO nanoparticle synthesis, simultaneously targeting high yield and high antibacterial activity. In fewer than 100 experiments, we developed a 1 kg day⁻¹ continuous synthesis for ZnO (with a space-time-yield of 62.4 kg day⁻¹ m⁻³), having an antibacterial activity comparable to hydrothermally synthesized nano-ZnO and cetrimonium bromide. Following this, we provide insights into the mechanistic factors underlying the performance-yield tradeoffs of synthesis and highlight the need for benchmarking machine learning models with traditional chemical engineering methods. Methods for increasing model accuracy at steep pareto fronts, in this case at yields close to 1 kg per day, should also be improved. To project the next steps for process scale-up and the potential advantages of this methodology, we conduct a scalability analysis in comparison to conventional batch production methods, in which there is a significant reduction in degrees of freedom. The proposed method has the potential to significantly reduce experimental costs, increase process efficiency and enhance material performance, which culminate to form a new pathway for materials discovery.

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New material innovation is limited by the time, expertise and cost of development. In the face of rapidly growing crises like pandemics, resource scarcity and climate change, we require new methods and methodologies to create and scale-up new technologies. In this work, we introduce an accelerated platform for material development, featuringadvanced machine learning methods, continuous microreactors and automation. As a case study, this platform was used to develop highly antimicrobial zinc oxide materials for coatings, with simultaneously optimized yield and performance. Continuous, high-shear microreactors and tangential flow filtration were employed at the lab-scale to enhance space time yields and scalability via number-up and scale-out, and culminating in ~ kg/day scale production.Decision-making was accelerated with the use of a multi-objective optimization algorithm for experimental design,allowing fast exploration of many variables without lengthy factorial designs and heuristic scale-up techniques.Furthermore, this platform was used to probe relationships between material characteristics (such as morphology and crystallinity), application performance and process parameters, showcasing the capability of this platform to make new discoveries and mechanistic insights. This approach features broad applicability to a range of materials and industries,including energy storage, pharmaceuticals and catalysis.@en