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M. Cristina Menziani

M. Cristina Menziani. University of Modena and Reggio Emilia. Quantitative Structure-Property Relationship ( Q SPR). Bulk properties Continuum scale properties Properties that can be measured but are not well understood. Atomistic scale Descriptors Structure Composition/Formulation

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M. Cristina Menziani

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  1. M. Cristina Menziani University of Modena and Reggio Emilia

  2. Quantitative Structure-Property Relationship (QSPR) • Bulk properties • Continuum scale properties • Properties that can be measured but are not well understood • Atomistic scale Descriptors • Structure • Composition/Formulation • Experimental data Mathematical correlations Simulations Atomistic Descriptors Mesoscale Structural characterization QSAR/QSPR Experimental data

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

  4. The QSPR workflow 1 Create Training Set • Get Structures and Data • Validate Structures Descriptors • Calculate Descriptors • Initial Data Analysis Build Models • Model Building • Model Management 2 Prerequisite: a set of compounds with known molecular descriptors and properties (features). 3 Model Validation and Prediction • Model Validation • Candidate Generation • Model-based Prediction 4

  5. QSPR provides an understanding of the effect of structure on property. 1 Allows to make predictions leading to materials with properties optimized for the intended application. 2 may be used for a fast initial screening to identify candidate materials for further, more time consuming, modeling or experimental studies. 3

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