Active machine learning poised to revolutionize chemical engineering research, but barriers to adoption remain

Active machine learning combines machine learning with design of experiments to enhance chemical engineering research efficiency.
It offers flexibility and superior performance over traditional methods, but adoption is still limited.
Main barriers are convincing researchers, ensuring data flexibility, and enhancing algorithm robustness.
Collaboration between ML experts and chemical engineers needed to customize algorithms.
Future looks bright if challenges around awareness, constraints, and synthesizability are addressed.

phys.org

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Posted 9/29/2023, 5:09:03 PM