In this work we demonstrate that the experimentally measured area specific resistance and oxygen surface exchange of solid oxide fuel cell cathode perovskites are strongly correlated with the first-principles calculated oxygenp-band center and vacancy formation energy. These quantities are therefore descriptors of catalytic activity that can be used in the first-principles design of new SOFC cathodes.

The Materials Genome Initiative (MGI) advanced a new paradigm for materials discovery and design, namely that the pace of new materials deployment could be accelerated through complementary efforts in theory, computation, and experiment. Along with numerous successes, new challenges are inviting researchers to refocus the efforts and approaches that were originally inspired by the MGI. In May 2017, the National Science Foundation sponsored the workshop “Advancing and Accelerating Materials Innovation Through the Synergistic Interaction among Computation, Experiment, and Theory: Opening New Frontiers” to review accomplishments that emerged from investments in science and infrastructure under the MGI, identify scientific opportunities in this new environment, examine how to effectively utilize new materials innovation infrastructure, and discuss challenges in achieving accelerated materials research through the seamless integration of experiment, computation, and theory. This article summarizes key findings from the workshop and provides perspectives that aim to guide the direction of future materials research and its translation into societal impacts.

There has been a growing effort to replace manual extraction of data from research papers with automated data extraction based on natural language processing, language models, and recently, large language models (LLMs). Although these methods enable efficient extraction of data from large sets of research papers, they require a significant amount of up-front effort, expertise, and coding. In this work, we propose the method that can fully automate very accurate data extraction with minimal initial effort and background, using an advanced conversational LLM. consists of a set of engineered prompts applied to a conversational LLM that both identify sentences with data, extract that data, and assure the data’s correctness through a series of follow-up questions. These follow-up questions largely overcome known issues with LLMs providing factually inaccurate responses. can be applied with any conversational LLMs and yields very high quality data extraction. In tests on materials data, we find precision and recall both close to 90% from the best conversational LLMs, like GPT-4. We demonstrate that the exceptional performance is enabled by the information retention in a conversational model combined with purposeful redundancy and introducing uncertainty through follow-up prompts. These results suggest that approaches similar to , due to their simplicity, transferability, and accuracy are likely to become powerful tools for data extraction in the near future. Finally, databases for critical cooling rates of metallic glasses and yield strengths of high entropy alloys are developed using .

This work demonstrates the essential role of particle size and crossover hydrogen on the degradation of platinum polymer electrolyte membrane fuel cell (PEMFC) cathodes. One of the major barriers to implementation of practical PEMFCs is the degradation of the cathode catalyst under operating conditions. This work combines both experimental and theoretical techniques to develop a validated and thermodynamically consistent kinetic model for the coupling of degradation and the catalyst particle size distribution. Our model demonstrates that, due to rapid changes in the Gibbs–Thomson energy, particle size effects dominate degradation for ∼2 nm particles but play almost no role for ∼5 nm particles. This result can help guide synthesis of more stable distributions. We also identify the effect of hydrogen molecules that cross over from the anode, demonstrating that in the presence of this crossover hydrogen surface area loss is greatly enhanced. We demonstrate that crossover hydrogen changes the surface area loss mechanism from coarsening to platinum loss through dissolution and precipitation off of the carbon support.

Metal fluorides and oxides can store multiple lithium ions through conversion chemistry to enable high-energy-density lithium-ion batteries. However, their practical applications have been hindered by an unusually large voltage hysteresis between charge and discharge voltage profiles and the consequent low-energy efficiency (<80%). The physical origins of such hysteresis are rarely studied and poorly understood. Here we employ in situ X-ray absorption spectroscopy, transmission electron microscopy, density functional theory calculations, and galvanostatic intermittent titration technique to first correlate the voltage profile of iron fluoride (FeF₃), a representative conversion electrode material, with evolution and spatial distribution of intermediate phases in the electrode. The results reveal that, contrary to conventional belief, the phase evolution in the electrode is symmetrical during discharge and charge. However, the spatial evolution of the electrochemically active phases, which is controlled by reaction kinetics, is different. We further propose that the voltage hysteresis in the FeF₃ electrode is kinetic in nature. It is the result of ohmic voltage drop, reaction overpotential, and different spatial distributions of electrochemically active phases (i.e., compositional inhomogeneity). Therefore, the large hysteresis can be expected to be mitigated by rational design and optimization of material microstructure and electrode architecture to improve the energy efficiency of lithium-ion batteries based on conversion chemistry.