Data Science and Machine Learning
Data ridden systems are a frequent occurrence in all fields of human endeavor as computers have the means to store an incredible amount of data. Biological engineering, of particular interest to us, is no exception in this regard as advances in measurement techniques have helped collect massive amounts of data in numerous areas in which chemical engineers are active. Venkatasubramanian provides an interesting perspective of artificial intelligence in chemical engineering.81 He is cautiously optimistic, and observes that data science and machine learning with suitable infusion of first principles could be an attractive combination for chemical engineering applications. Diagnostic issues in health science are indeed a promising area of application. Verma, in quest of the source of peripheral neuropathy from the use of Vincristine in the treatment of leukemic cancer, used machine learning to identify a handful of pain associated metabolites.82 Early detection of such metabolites from blood samples could lead to drug dosage adjustments for improving the quality of life.
Lumping of chemical species in systems with a very large number of species has been of interest to chemical reaction engineers. When nonlinear reaction kinetics is involved, lumping strategies could be of interest using data science and machine learning. Similarly, order parameters or collective variables have been of interest in molecular systems towards reducing their dimension as a means to study rare events such as nucleation. Data science and machine learning could be a potential source to elucidate a small number of collective variables in terms of which the free energy of the system can be determined. While such applications abound, the likelihood of pedestrian usage of the methodology cannot be ignored.