11 Integration of Modelling at Various Length and Time Scales 225 11.2 Structure-Activity and Structure-Property Approaches Quantitative structure activity and property relationships (QSAR/QSPR) have long been used with great success in the life sciences.Based on exper- imental 'training set'data,correlations can be established between a range of molecular descriptors and biological activity.These correlations may take the form of equations derived by methods such as the Genetic Function Ap- proximation [11.4,or neural networks.QSAR methods have proved to be powerful tools for the design of molecular libraries,investigating similarity and diversity as well as predicting properties. Not surprisingly,such tools have also been applied successfully in a va- riety of materials cases as well [11.5,11.6.These statistical methods allow experimental information to be mined for important correlations,which can lead to deeper understanding of a material and optimised products.The cor- relations can be used to help design better materials.These new materials can be screened using the simulation methods and so an effective feedback loop is created which efficiently leads to new materials. However,the complexity and multiscale nature of many materials and their properties pose particular challenges in the application of QSAR meth- ods,which need to be address in future Materials QSAR tools.Firstly,there are many different materials classes with potentially very different sets of descriptors relevant to them.There is little knowledge so far about which are the most important ones relating for example to the prediction of per- meability properties of polymer materials.Secondly,the calculation of the descriptors may involve simulations using methods at various scales,some of which may be computationally expensive. 11.3 Atomistic and Mesoscale Simulations and Their Parameterisation Quantum,atomistic and mesoscale simulations provide valuable insights into the detailed physico-chemical behaviour of molecules and materials,and there are many properties,which can be determined directly from each,includ- ing structure,energies,stability,activity,diversity,solubility,adhesion,ad- sorption,diffusion,mechanical constants,spectra,and morphology.Ab initio quantum methods have the advantage that they can in principle be used for any element in the periodic table without specific parameterisation.They have been extensively developed so that one is now able to handle systems of a few hundred atoms routinely.For larger systems,however,methods re- quiring parameterisation are inevitable.In the following,we focus on force field developments for atomistic simulations and parameter determination for mesoscale simulations.11 Integration of Modelling at Various Length and Time Scales 225 11.2 Structure-Activity and Structure-Property Approaches Quantitative structure activity and property relationships (QSAR/QSPR) have long been used with great success in the life sciences. Based on exper￾imental ‘training set’ data, correlations can be established between a range of molecular descriptors and biological activity. These correlations may take the form of equations derived by methods such as the Genetic Function Ap￾proximation [11.4], or neural networks. QSAR methods have proved to be powerful tools for the design of molecular libraries, investigating similarity and diversity as well as predicting properties. Not surprisingly, such tools have also been applied successfully in a va￾riety of materials cases as well [11.5, 11.6]. These statistical methods allow experimental information to be mined for important correlations, which can lead to deeper understanding of a material and optimised products. The cor￾relations can be used to help design better materials. These new materials can be screened using the simulation methods and so an effective feedback loop is created which efficiently leads to new materials. However, the complexity and multiscale nature of many materials and their properties pose particular challenges in the application of QSAR meth￾ods, which need to be address in future Materials QSAR tools. Firstly, there are many different materials classes with potentially very different sets of descriptors relevant to them. There is little knowledge so far about which are the most important ones relating for example to the prediction of per￾meability properties of polymer materials. Secondly, the calculation of the descriptors may involve simulations using methods at various scales, some of which may be computationally expensive. 11.3 Atomistic and Mesoscale Simulations and Their Parameterisation Quantum, atomistic and mesoscale simulations provide valuable insights into the detailed physico-chemical behaviour of molecules and materials, and there are many properties, which can be determined directly from each, includ￾ing structure, energies, stability, activity, diversity, solubility, adhesion, ad￾sorption, diffusion, mechanical constants, spectra, and morphology. Ab initio quantum methods have the advantage that they can in principle be used for any element in the periodic table without specific parameterisation. They have been extensively developed so that one is now able to handle systems of a few hundred atoms routinely. For larger systems, however, methods re￾quiring parameterisation are inevitable. In the following, we focus on force field developments for atomistic simulations and parameter determination for mesoscale simulations