Extracting accurate materials data from research papers with conversational language models and prompt engineering.

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 ChatExtract method that can fully automate very accurate data extraction with minimal initial effort and background, using an advanced conversational LLM. ChatExtract 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. ChatExtract 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 ChatExtract, 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 ChatExtract.

Strong Room-Temperature Ferromagnetism in Ultrathin NiOOH Nanosheets through Surfactant Manipulation.

Two-dimensional (2D) ferromagnetic (FM) materials with nanoscale thickness and spontaneous net magnetization have emerged as a promising class of functional materials for applications in next-generation spintronics, quantum processing, and data storage devices. However, most 2D materials exhibit weak FM even at low temperatures, limiting their potential applications in many technological fields. The fabrication of strong room-temperature FM 2D materials is highly desirable for the development of practical applications. Here, we demonstrate an ionic layer epitaxy strategy to synthesize few-layered NiOOH nanosheets with strong room-temperature FM and a saturation magnetization up to 409.86 emu cm-3 at 300 K. The results are consistent with the ab initio predictions of a stable FM NiOOH nanolayer structure with an FM configuration. The FM strength of the NiOOH nanosheets can be tuned by controlling the surfactant monolayer density and annealing. This work offers a promising strategy for achieving strong high-temperature FM in 2D materials for spintronic applications.

Bandgap Pressure Coefficient of a CH3NH3PbI3 Thin Film Perovskite.

Recent scientific interest in examining the bandgap evolution of a MAPbI3 hybrid perovskite by applying hydrostatic pressure has mostly focused on a room-temperature tetragonal phase. In contrast, the pressure response of a low-temperature orthorhombic phase (OP) of MAPbI3 has not been explored and understood. In this research, we investigate for the first time how hydrostatic pressure alters the electronic landscape of the OP of MAPbI3. Pressure studies using photoluminescence combined with calculations within density functional theory at zero temperature allowed us to identify the main physical factors affecting the bandgap evolution of the OP of MAPbI3. The negative bandgap pressure coefficient was found to be strongly dependent on the temperature (α120K = -13.3 ± 0.1 meV/GPa, α80K = -29.8 ± 0.1 meV/GPa, and α40K = -36.3 ± 0.1 meV/GPa). Such dependence is related to the changes in the Pb-I bond length and geometry in the unit cell as the atomic configuration approaches the phase transition as well as the increasing phonon contribution to octahedral tilting as the temperature increases.

Machine learning for impurity charge-state transition levels in semiconductors from elemental properties using multi-fidelity datasets.

Quantifying charge-state transition energy levels of impurities in semiconductors is critical to understanding and engineering their optoelectronic properties for applications ranging from solar photovoltaics to infrared lasers. While these transition levels can be measured and calculated accurately, such efforts are time-consuming and more rapid prediction methods would be beneficial. Here, we significantly reduce the time typically required to predict impurity transition levels using multi-fidelity datasets and a machine learning approach employing features based on elemental properties and impurity positions. We use transition levels obtained from low-fidelity (i.e., local-density approximation or generalized gradient approximation) density functional theory (DFT) calculations, corrected using a recently proposed modified band alignment scheme, which well-approximates transition levels from high-fidelity DFT (i.e., hybrid HSE06). The model fit to the large multi-fidelity database shows improved accuracy compared to the models trained on the more limited high-fidelity values. Crucially, in our approach, when using the multi-fidelity data, high-fidelity values are not required for model training, significantly reducing the computational cost required for training the model. Our machine learning model of transition levels has a root mean squared (mean absolute) error of 0.36 (0.27) eV vs high-fidelity hybrid functional values when averaged over 14 semiconductor systems from the II-VI and III-V families. As a guide for use on other systems, we assessed the model on simulated data to show the expected accuracy level as a function of bandgap for new materials of interest. Finally, we use the model to predict a complete space of impurity charge-state transition levels in all zinc blende III-V and II-VI systems.