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Abstract 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.

In this work, the optoelectronic properties of the getchellite crystal, a layered semiconducting disordered alloy with the chemical formula AsSbS3, are probed by a combination of complementary spectroscopic techniques, i.e., x-ray photoelectron spectroscopy together with a photoreflectance and a transmission spectroscopy. The experimental results are supported by a calculation based on density functional theory (DFT). The sample is an intrinsically p-type semiconductor whose optical properties are dominated by a direct transition. The energy of this transition is highly decreasing, by 0.22 eV, when the sample temperature increases from 20 to 300 K. A calculated band structure together with the transmission results reveals that of about 50 meV below the direct optical transition, an indirect one occurs associated with the fundamental bandgap of AsSbS3. Finally, the change in the fundamental bandgap character from indirect to direct during the reduction in material thickness from bulk to monolayer is demonstrated by the DFT calculations.

Abstract Transition metal dichalcogenides (TMDs), especially in two-dimensional (2D) form, exhibit many properties desirable for device applications. However, device performance can be hindered by the presence of defects. Here, we combine state of the art experimental and computational approaches to determine formation energies and charge transition levels of defects in bulk and 2D MX2 (M = Mo or W; X = S, Se, or Te). We perform deep level transient spectroscopy (DLTS) measurements of bulk TMDs. Simultaneously, we calculate formation energies and defect levels of all native point defects, which enable identification of levels observed in DLTS and extend our calculations to vacancies in 2D TMDs, for which DLTS is challenging. We find that reduction of dimensionality of TMDs to 2D has a significant impact on defect properties. This finding may explain differences in optical properties of 2D TMDs synthesized with different methods and lays foundation for future developments of more efficient TMD-based devices.