CAREER: Optimization Under Property Prediction Uncertainity in Molecular Design

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Abstract - Maranas - 9701771 The purpose of this project is to design molecular products which optimally meet a set of design objectives while accounting for inaccuracies in the employed structure-property relations. Existing approaches for computer-aided molecular design suffer from two limitations: (1) they may or may not converge to the best molecular design depending on their initialization and the adopted search strategy; and (2) even if by chance the mathematically best molecular design is found, it is still unclear if the best molecular is truly at hand due to the uncertainty in the structure-property expressions. This research aims at overcoming these shortcoming. To this end, a blend of chance-constrained programming, statistical analysis, mixed-integer linear and nonlinear programming algorithms, and deterministic global optimization will be utilized. By eliminating the pitfall of unknowingly converging to suboptimal molecular designs and quantifying the effect of property prediction uncertainty on obtained molecular designs, the PI expects to improve the chances and expedite the identification of molecular products. This will permit experimental efforts to be concentrated on only the most promising molecular candidate products. The educational initiatives planned in this CAREER grant are two-fold: (a) course development, and (b) student mentoring, advising and introduction to research. By taking advantage of the planned research on optimization in molecular design and prior work on molecular structure identification, a course on molecular design (which currently does not exist at Penn State) is planned to be offered in two years. A course on advanced process synthesis and optimization will shortly be submitted for approval by the Faculty Senate to fill a vacancy in advanced process synthesis courses in the Chemical Engineering curriculum at Penn State. The course will follow a preliminary version taught over the summer semester and will borrow heavily from the PI's resea rch work in optimization.