Accelerating the Pace of Oxygen Evolution Reaction Catalyst Discovery through Megalibraries.

Iridium (Ir) catalysts are essential for the acidic oxygen evolution reaction (OER) in proton-exchange membrane water electrolyzers (PEMWEs), but their high cost, scarcity, and geographical concentration limit large-scale adoption. In addition, the discovery of non-Ir alternatives is slow due to the vast design space possible. Here, a "megalibrary" is used to explore the catalytic activity of ∼156 million distinct nanostructures comprised of Ru, Co, Mn, and Cr to find alternatives to Ir catalysts for OER.

How the AI-assisted discovery and synthesis of a ternary oxide highlights capability gaps in materials science.

Exploratory synthesis has been the main generator of new inorganic materials for decades. However, our Edisonian and bias-prone processes of synthetic exploration alone are no longer sufficient in an age that demands rapid advances in materials development. In this work, we demonstrate an end-to-end attempt towards systematic, computer-aided discovery and laboratory synthesis of inorganic crystalline compounds as a modern alternative to purely exploratory synthesis.

High-throughput ab initio design of atomic interfaces using InterMatch.

Forming a hetero-interface is a materials-design strategy that can access an astronomically large phase space. However, the immense phase space necessitates a high-throughput approach for an optimal interface design. Here we introduce a high-throughput computational framework, InterMatch, for efficiently predicting charge transfer, strain, and superlattice structure of an interface by leveraging the databases of individual bulk materials.

Agents for sequential learning using multiple-fidelity data.

Sequential learning for materials discovery is a paradigm where a computational agent solicits new data to simultaneously update a model in service of exploration (finding the largest number of materials that meet some criteria) or exploitation (finding materials with an ideal figure of merit). In real-world discovery campaigns, new data acquisition may be costly and an optimal strategy may involve using and acquiring data with different levels of fidelity, such as first-principles calculation to supplement an experiment. In this work, we introduce agents which can operate on multiple data fidelities, and benchmark their performance on an emulated discovery campaign to find materials with desired band gap values.

Random sub-diffusion and capture of genes by the nuclear pore reduces dynamics and coordinates inter-chromosomal movement.

Hundreds of genes interact with the yeast nuclear pore complex (NPC), localizing at the nuclear periphery and clustering with co-regulated genes. Dynamic tracking of peripheral genes shows that they cycle on and off the NPC and that interaction with the NPC slows their sub-diffusive movement. Furthermore, NPC-dependent inter-chromosomal clustering leads to coordinated movement of pairs of loci separated by hundreds of nanometers.

Data-driven studies of magnetic two-dimensional materials.

We use a data-driven approach to study the magnetic and thermodynamic properties of van der Waals (vdW) layered materials. We investigate monolayers of the form [Formula: see text], based on the known material [Formula: see text], using density functional theory (DFT) calculations and machine learning methods to determine their magnetic properties, such as magnetic order and magnetic moment. We also examine formation energies and use them as a proxy for chemical stability.