Qualitative or quantitative characterization of a coating surface

A computer system comprising at least one processor and computer-interpretable instructions which, when executed by the at least one processor, cause the at least one processor to:

• access a database comprising associations of one or more of qualitative and quantitative characterizations of coating surfaces and one or more parameters selected from a group comprising one or more of:
• one or more of the components of the coating composition used for producing the respective coating surface;
• one or more of relative and absolute amounts of one or more of the components;
• manufacturing-process parameters of the coating composition;
• and application-process parameters used for creating the coating surfaces;
• train a machine learning model on the associations of the coating surface characterizations with the one or more parameters in the database for providing a predictive model having learned to correlate the one or more of the qualitative and the quantitative characterizations of one or more coating surfaces with one or more of the parameters stored in association with the respective coating surface characterizations;
• and one or more of:
• provide a composition-quality-prediction program which comprises a first predictive model, the composition-quality-prediction program being configured for using the first predictive model for predicting the properties of a coating surface to be produced from one or more input parameters selected from a group comprising one or more of:
• one or more components of a coating composition to be used for producing a coating surface;
• one or more of relative and absolute amounts of one or more of the components;
• manufacturing-process parameters to be used for preparing the coating composition;
• and application-process parameters to be used for creating the coating surface;
• and
• provide a composition-specification-prediction program which comprises a second predictive model, the composition-specification-prediction program being configured for using the second predictive model for predicting, based on an input specifying at least a desired coating surface characterization and outputting one or more parameters related to a coating composition predicted to generate a coating surface having the input surface characterizations, the one or more output parameters being selected from a group comprising one or more of:
• one or more components of the coating composition;
• one or more of relative and absolute amounts of one or more of the components;
• manufacturing-process parameters to be used for preparing the coating composition;
• and application-process parameters for creating the coating surface,
• wherein the computer-interpretable instructions which, when executed by the at least one processor, further causes the at least one processor to training the machine learning model by using an active learning module for selecting a candidate coating composition specification from a set of candidate composition specifications which provides the strongest learning effect for the first predictive model and the second predictive model, the learning effect being provided as a result of the preparation of the selected candidate composition, empirical measurement of properties of the selected candidate composition, and retraining of one or more of the first predictive model and the second predictive model on training data comprising the empirically measured properties of the selected and prepared candidate composition,
• wherein the selection of the candidate coating composition specification by the active learning module comprises
• for each of the multiple candidate coating composition specifications:
• predicting properties of the specified candidate coating composition using the first predictive model of the composition-quality-prediction program;
• and
• determining an uncertainty score being indicative of the uncertainty of the first model with respect to the predicting;
• and
• using the one of the multiple candidate coating compositions having the maximum uncertainty score as the selected candidate coating composition specification.