The discovery of new materials has driven societal development throughout human history. Existing materials discovery strategies largely consist of serial or low-throughput experiments driven by chemical intuition or iterations on previous results and are too slow to match the materials complexity and diversity available through ever-developing synthesis techniques. Methods for high-throughput synthesis and screening, coupled with data-driven optimization processes are a route towards rapid materials discovery. We have developed a cantilever-free scanning probe lithography-based approach that, through the deposition of polymeric nanoreactors and thermal annealing, enables the synthesis of “megalibraries”, consisting of as many as 5 billion positionally encoded nanoparticles designed to systematically vary in composition and/or size. The megalibraries can be tailored to encompass a wide variety of alloy and phase-separated nanoparticles that are comprised of as many as 8 different elements with up to four phases and six interfaces. From these libraries, we have discovered new catalysts for energy relevant transformations, designed complex structural motifs into polyelemental nanoparticles both through computational and machine learning methods, and paved the way for rapid materials discovery and data generation.

Megalibraries: Tools for exploring and expanding the materials genome with big data and AI

ACS Fall 2021

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.