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Civilization and modern societies would not be possible without manmade materials. Considering their production volumes, their supporting role in nearly all industrial processes, and the impact of their sourcing and production on the environment, metals and alloys are and will be of prominent importance for the clean energy transition. The focus of materials discovery must move to more specialized, application-tailored green alloys that outperform the legacy materials not only in performance but also in sustainability and resource efficiency. This white paper summarizes a joint Canadian-German initiative aimed at developing a materials acceleration platform (MAP) focusing on the discovery of new alloy families that will address this challenge. We call our initiative the “Build to Last Materials Acceleration Platform” (B2L-MAP) and present in this perspective our concept of a three-tiered self-driving laboratory that is composed of a simulation-aided pre-selection module (B2L-select), an artificial intelligence (AI)-driven experimental lead generator (B2L-explore), and an upscaling module for durability assessment (B2L-assess). The resulting tool will be used to identify and subsequently demonstrate novel corrosion-resistant alloys at scale for three key applications of critical importance to an offshore, wind-driven hydrogen plant (reusable electrical contacts, offshore infrastructure, and oxygen evolution reaction catalysts).
Invited for this month’s cover are researchers from Bundesanstalt für Materialforschung und -prüfung (Federal Institute for Materials Research and Testing) in Germany, Friedrich Schiller University Jena, Université catholique de Louvain, University of Oregon, Science & Technology Facilities Council, RWTH Aachen University, Hoffmann Institute of Advanced Materials, and Dartmouth College. The cover picture shows a workflow for automatic bonding analysis with Python tools (green python). The bonding analysis itself is performed with the program LOBSTER (red lobster). The starting point is a crystal structure, and the results are automatic assessments of the bonding situation based on Crystal Orbital Hamilton Populations (COHP), including automatic plots and text outputs. Coordination environments and charges are also assessed. More information can be found in the Research Article by J. George, G. Hautier, and co-workers.
Automated bonding analysis software has been developed based on Crystal Orbital Hamilton Populations to facilitate high-throughput bonding analysis and machine-learning of bonding features. This work presents the software and discusses its applications to simple and complex materials such as GaN, NaCl, the oxynitrides XTaO2N (X=Ca, Ba, Sr) and Yb14Mn1Sb11.
Whereas the characterization of nanomaterials using different analytical techniques is often highly automated and standardized, the sample preparation that precedes it causes a bottleneck in nanomaterial analysis as it is performed manually. Usually, this pretreatment depends on the skills and experience of the analysts. Furthermore, adequate reporting of the sample preparation is often missing. In this overview, some solutions for techniques widely used in nano-analytics to overcome this problem are discussed. Two examples of sample preparation optimization by au-tomation are presented, which demonstrate that this approach is leading to increased analytical confidence. Our first example is motivated by the need to exclude human bias and focuses on the development of automation in sample introduction. To this end, a robotic system has been de-veloped, which can prepare stable and homogeneous nanomaterial suspensions amenable to a variety of well-established analytical methods, such as dynamic light scattering (DLS), small-angle X-ray scattering (SAXS), field-flow fractionation (FFF) or single-particle inductively coupled mass spectrometry (sp-ICP-MS). Our second example addresses biological samples, such as cells exposed to nanomaterials, which are still challenging for reliable analysis. An air–liquid interface has been developed for the exposure of biological samples to nanomaterial-containing aerosols. The system exposes transmission electron microscopy (TEM) grids under reproducible conditions, whilst also allowing characterization of aerosol composition with mass spectrometry. Such an approach enables correlative measurements combining biological with physicochemical analysis. These case studies demonstrate that standardization and automation of sample preparation setups, combined with appropriate measurement processes and data reduction are crucial steps towards more reliable and reproducible data.