Optimization-Free Inverse Design of High-Dimensional Nanoparticle Electrocatalysts Using Multi-target Machine Learning

Inverse design that directly predicts multiple structural characteristics of nanomaterials based on a set of desirable properties is essential for translating computational predictions into laboratory experiments, and eventually into products. This is challenging due to the high-dimensionality of nanomaterials data which causes an imbalance in the mapping problem, where too few properties are available to predict too many features. In this paper we use multi-target machine learning to directly map the structural features and property labels, without the need for exhaustive data sets or external optimization, and explore the impact of more aggressive feature selection to manage the mapping function. We find that systematically reducing the dimensionality of the feature set improves the accuracy and generalizability of inverse models when interpretable importance profiles from the corresponding forward predictions are used to prioritize inclusion. This allows for a balance between accuracy and efficiency to be established on a case-by-case basis, but raises new questions about the role of domain knowledge and pragmatic preferences in feature prioritization strategies.