1 M. Cristina Menziani

2 Quantitative Structure-Property Relationship (QSPR) Atomistic scale Descriptors Structure Composition/Formulation Experimental data Atomistic scale Descriptors Structure Composition/Formulation Experimental data Bulk propertiesBulk properties Continuum scale propertiesContinuum scale properties Properties that can be measured but are not well understoodProperties that can be measured but are not well understood Bulk propertiesBulk properties Continuum scale propertiesContinuum scale properties Properties that can be measured but are not well understoodProperties that can be measured but are not well understood Mathematical correlations Mathematical correlations Structuralcharacterization Mesoscale Atomistic Simulations Descriptors ExperimentaldataExperimentaldata

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4 Comparison of Simulated and Experimental Densities of Polymers V = f (p) V 1 P1 (V 1 ), P2 (V1), …,Pn (V1) V 2 P1 (V2), P2 (V2), …,Pn (V2) V 3 P1 (V3), P2 (V3), …,Pn (V3) …… … … … V m P1 (Vm), P2 (Vm), …,Pn (Vm) p are structure-derived descriptors

5 The QSPR workflow Model Validation and Prediction Model Validation Model Validation Candidate Generation Candidate Generation Model-based Prediction Model-based Prediction Create Training Set Get Structures and Data Get Structures and Data Validate Structures Validate StructuresDescriptors Calculate Descriptors Calculate Descriptors Initial Data Analysis Initial Data Analysis Build Models Model Building Model Building Model Management Model Management Prerequisite: a set of compounds with known molecular descriptors and properties (features). Prerequisite:

6 QSPR provides an understanding of the effect of structure on property. may be used for a fast initial screening to identify candidate materials for further, more time consuming, modeling or experimental studies. Allows to make predictions leading to materials with properties optimized for the intended application.