Systems and methods with machine learned dataset embedding for data fusion of material property datasets

Abstract

A machine learning system includes a processor and a memory communicably coupled to the processor. The memory stores machine-readable instructions that, when executed by the processor, cause the processor to select a training dataset comprising training material compositions and tagged material property values, select at least two material property datasets comprising material compositions with corresponding material property values, and embed the training material compositions and the material compositions of the at least two material property datasets into a chemical space of a machine learning module. The memory also stores machine-readable instructions that, when executed by the processor, cause the processor to predict, based at least in part on the training material compositions and the material compositions of the at least two material property datasets embedded in the chemical space, property values for corresponding material compositions in the at least two material property datasets.