A computer-implemented method comprising: - receiving information about a multitude of formulations, the information comprising composition information about compositions of formulations, and property information about one or more properties of formulations, - generating training data from the information about the multitude of formulations, the training data comprising composition data, and property data; - providing a machine learning model; - training the machine learning model, thereby generating a trained machine learning model (tMLM), - storing the trained machine learning model (tMLM) and/or outputting the trained machine learning model (tMLM) and/or using the trained machine learning model (tMLM) for prediction purposes, characterized in that the training comprises: ∘ inputting composition data into the machine learning model, wherein the machine learning model is configured to ▪ generate a compressed representation of the formulation, at least partially on the basis of the composition data, ▪ determine predicted property data (pPD), at least partially on the basis of the compressed representation of the formulation, ▪ generate reconstructed composition data (rCD), at least partially on the basis of the compressed representation of the formulation, ∘ computing a loss by means of one or more loss function(s), the loss function(s) quantifying deviations between at least a part of the composition data and the reconstructed composition data (rCD), and between at least a part of the property data and the predicted property data (pPD), o modifying parameters of the machine learning model so that the loss is minimized - receiving information about a multitude of formulations, the information comprising composition information about compositions of formulations, and property information about one or more properties of formulations, - generating training data from the information about the multitude of formulations, the training data comprising composition data, and property data; - providing a machine learning model; - training the machine learning model, thereby generating a trained machine learning model (tMLM), - storing the trained machine learning model (tMLM) and/or outputting the trained machine learning model (tMLM) and/or using the trained machine learning model (tMLM) for prediction purposes, ∘ inputting composition data into the machine learning model, wherein the machine learning model is configured to ▪ generate a compressed representation of the formulation, at least partially on the basis of the composition data, ▪ determine predicted property data (pPD), at least partially on the basis of the compressed representation of the formulation, ▪ generate reconstructed composition data (rCD), at least partially on the basis of the compressed representation of the formulation, ▪ generate a compressed representation of the formulation, at least partially on the basis of the composition data, ▪ determine predicted property data (pPD), at least partially on the basis of the compressed representation of the formulation, ▪ generate reconstructed composition data (rCD), at least partially on the basis of the compressed representation of the formulation, ∘ computing a loss by means of one or more loss function(s), the loss function(s) quantifying deviations between at least a part of the composition data and the reconstructed composition data (rCD), and between at least a part of the property data and the predicted property data (pPD), o modifying parameters of the machine learning model so that the loss is minimized