Discovery of new materials using combinatorial synthesis and high-throughput characterization of thin-film materials libraries combined with computational methods

Alfred Ludwig

doi:10.1038/s41524-019-0205-0

Abstract

This perspective provides an experimentalist’s view on materials discovery in multinary materials systems—from nanoparticles over thin films to bulk—based on combinatorial thin-film synthesis and high-throughput characterization in connection with high-throughput calculations and materials informatics. Complete multinary materials systems as well as composition gradients which cover all materials compositions necessary for verification/falsification of hypotheses and predictions are efficiently fabricated by combinatorial synthesis of thin-film materials libraries. Automated high-quality high-throughput characterization methods enable comprehensive determination of compositional, structural and (multi)functional properties of the materials contained in the libraries. The created multidimensional datasets enable data-driven materials discoveries and support efficient optimization of newly identified materials, using combinatorial processing. Furthermore, these datasets are the basis for multifunctional existence diagrams, comprising correlations between composition, processing, structure and properties, which can be used for the design of future materials.

Highlights

The discovery of new materials and their rapid optimization are necessary to enable future technological developments in areas such as sustainable energy technologies and energy-efficient processes: e.g., the envisaged hydrogen economy relies on materials discoveries like light-absorbing, catalytic and stable photoelectrodes for solar water splitting, lightweight hydrogen storage materials or noble-metal-free fuel cell catalysts
The following examples illustrate discoveries made using thin-film materials libraries (MLs), in some cases supported by computational materials science
Findings from thin-film MLs explorations can be transferred to bulk

Summary

INTRODUCTION

The discovery of new (multinary) materials and their rapid optimization are necessary to enable future technological developments in areas such as sustainable energy technologies and energy-efficient processes: e.g., the envisaged hydrogen economy relies on materials discoveries like light-absorbing, catalytic and stable photoelectrodes for solar water splitting, lightweight hydrogen storage materials or noble-metal-free fuel cell catalysts. The potential for materials discoveries is high, as the periodic table offers numerous elements which can be combined in multinary material systems, offering an almost unlimited, and yet mostly unexplored search space for new materials. This is simultaneously a promise and a challenge as not all possible materials can be explored, even with the most efficient highthroughput technologies. The multi-dimensional search space for materials discoveries comprises both intrinsic (e.g., composition, crystal structure, functional properties (e.g., saturation magnetization in ferromagnets)) and extrinsic properties (e.g., microstructure, functional properties (e.g., coercivity in ferromagnets)) As this search space is almost unlimited, a down-selection of the materials systems to be addressed in experiments is required. From the computational results a feasible list of candidate materials for combinatorial exploration can be deduced, i.e., lists of tens or hundreds of materials

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EXEMPLARY RESULTS

CONCLUSIONS AND OUTLOOK