Inverse Design of Nanoparticles Using Charge Transfer Properties and Multi-Target Machine Learning

Inverse design [1, 2] that prescribes a target structure is a primary objective in materials informatics, and the ultimate goal of much academic and industrial research. However, the majority of materials informatics uses machine learning (ML) to make forward predictions of a property of a material (the target label) based on the structural characteristics (the features); so-called structure/property relationships. Inverse design involves property/structure relationships that are highly desirable since a researcher usually knows what properties they need for a particular application and want a recipe of what they should be attempting to make in the lab. This is challenging, and even more complicated in nanomaterials design, where the finite sizes and multitude of shapes mean the design space is larger.[3] This goal has been approached in the past using conventional structure/property relationships generated with ML and screening of hypothetical candidate materials using an additional step to optimize and rank the outcomes. Unfortunately, this approach suffers from too much specificity since structure/property relationships typically involve many structural features but only one target property label. This makes inverting the problem difficult as there will be only one known variable (property) and numerous unknown variables (structures) making the solution intractable.