Polymeric membrane design is a multidimensional process involving selection of membrane materials and optimization of fabrication conditions from an infinite candidate space. It is impossible to explore the entire space by trial-and-error experimentation. Here, we present a membrane design strategy utilizing machine learning-based Bayesian optimization to precisely identify the optimal combinations of unexplored monomers and their fabrication conditions from an infinite space. We developed ML models to accurately predict water permeability and salt rejection from membrane monomer types (represented by the Morgan fingerprint) and fabrication conditions. We applied Bayesian optimization on the built ML model to inversely identify sets of monomer/fabrication condition combinations with the potential to break the upper bound for water/salt selectivity and permeability. We fabricated eight membranes under the identified combinations and found that they exceeded the present upper bound. Our findings demonstrate that ML-based Bayesian optimization represents a paradigm shift for next-generation separation membrane design.

Inefficiencies and imprecise input control in agriculture have caused devastating consequences to ecosystems. Urban controlled environment agriculture (CEA) is a proposed approach to mitigate the impacts of cultivation, but precise control of inputs (i.e., nutrient, water, etc.) is limited by the ability to monitor dynamic conditions. Current mechanistic and physiological plant growth models (MPMs) have not yet been unified and have uncovered knowledge gaps of the complex interplay among control variables. Moreover, because of their specificity, MPMs are of limited utility when extended to additional plant species or environmental conditions. Simultaneously, although machine learning (ML) can uncover latent interactions across conditions, phenotyping bottlenecks have hindered successful application. To bridge these gaps, we propose an integrative approach whereby MPMs are used to construct the foundations of ML algorithms, reducing data requirements and costs, and ML is used to elucidate parameters and causal inference in MPM. This review highlights research about control and automation in CEA, synthesizing literature into a framework whereby ML, MPM, and biofeedback inform what we call dynamically controlled environment agriculture (DCEA). We highlight synergistic characteristics of MPM and ML to illustrate that a DCEA framework could contribute to urban resilience, human health, and optimized productivity and nutritional content.