A multidisciplinary ORNL team used expertise in synthetic biology, AI-driven analysis, chemistry, neutrons and materials science to identify new members of a family of enzymes with a natural affinity for degrading synthetic nylon polymers.
We successfully utilized OCLF ORNL GPU computing resources for efficient uncertainty analysis, which addressed the computational overhead caused by our proposed probabilistic models.
