Materials synthesis and optimization are tedious and labor-intensive. Consequently, we need a paradigm shift toward the prediction of materials performance without the necessity to experimentally optimize them. Developing methodologies using machine learning, a subfield of artificial intelligence, for the performance prediction and optimization of separation materials is high on the sustainability agenda. Experimental design in combination with machine learning algorithms is employed to develop materials with a reduced environmental footprint.
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