Journal
CHEMICAL SCIENCE
Volume 9, Issue 9, Pages 2398-2412Publisher
ROYAL SOC CHEMISTRY
DOI: 10.1039/c7sc04679k
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Funding
- NSF [CHE-1361296]
- Joint Center for Energy Storage Research (JCESR) a Department of Energy, Energy Innovation Hub
- NIH [1 R01 GM121383]
- Center for High Performance Computing at the University of Utah
- Division Of Chemistry
- Direct For Mathematical & Physical Scien [1361296] Funding Source: National Science Foundation
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Multivariate Linear Regression (MLR) models utilizing computationally-derived and empirically-derived physical organic molecular descriptors are described in this review. Several reports demonstrating the effectiveness of this methodological approach towards reaction optimization and mechanistic interrogation are discussed. A detailed protocol to access quantitative and predictive MLR models is provided as a guide for model development and parameter analysis.
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