Machine learning-accelerated design and synthesis of polyelemental heterostructures

In materials discovery efforts, synthetic capabilities far outpace the ability to extract meaningful data from them. To bridge this gap, machine learning methods are necessary to reduce the search space for identifying desired materials. Here, we present a machine learning-driven, closed-loop experimental process to guide the synthesis of polyelemental nanomaterials with targeted structural properties. By leveraging data from an eight-dimensional chemical space (Au-Ag-Cu-Co-Ni-Pd-Sn-Pt) as inputs, a Bayesian optimization algorithm is used to suggest previously unidentified nanoparticle compositions that target specific interfacial motifs for synthesis, results of which are iteratively shared back with the algorithm. This feedback loop resulted in successful syntheses of 18 heterojunction nanomaterials that are too complex to discover by chemical intuition alone, including extremely chemically complex biphasic nanoparticles reported to date. Platforms like the one developed here are poised to transform materials discovery across a wide swath of applications and industries.

Automated summary

In this study, a machine learning-driven experimental process was proposed to guide the synthesis of nanomaterials with specific structural properties, resulting in the successful synthesis of 18 heterojunction nanomaterials that are too complex to discover solely based on chemical intuition. This approach has the potential to revolutionize materials discovery in a wide range of applications and industries.