Input Consistency Regularization for Modeling Negative Thermal Expansion Characteristics of A2M3O12 Family of Compounds

The prediction confidence is one of the goals of any machine learning-based study with no respect if this is distinguished as the aim of the study or is the associated desired concomitant. The possibility do not import the additional error into the pre-experimental estimation of the studied characteristics should be the goal of machine learning-based approaches in any case limited in their accuracy by the precision and confidence of the experimental data. In this study, we consider the approaches related to the input consistency regularization which may provide with the required enhancement in the prediction confidence and are able to recoup the part of the experimental error associated with obtaining the data using the methods of different precision. The methodology of regularization of input data consistency is considered in relation to the problem of the predictive modeling of the functional characteristics of A2M3O12 family of ceramics with negative thermal expansion (NTE) property. The methodological part of this study includes several problems. The Hessian-based analysis of the loss function landscape was considered as the criterion of the generalizability and model performance. The continuity of the property change as a function of the data description coupled with the p-values for the experiment-prediction output were considered as the auxiliary criteria concerned with the input consistency regularization.