DOI: 10.6084/m9.figshare.29319638
title: Supporting data and codes to a paper entitled: <i>Tartrate-Assisted Ionic Layer Epitaxy for General Synthesis of Two-dimensional Nanostructures </i>by Zhang, Ziyi; Wang, Derui; Polak, Maciej; Carlos, Corey; Dong, Yutao; Morgan, Dane; Wang, Xudong

description: Supporting data and codes to a paper entitled: <i>Tartrate-Assisted Ionic Layer Epitaxy for General Synthesis of Two-dimensional Nanostructures </i>by Zhang, Ziyi; Wang, Derui; Polak, Maciej; Carlos, Corey; Dong, Yutao; Morgan, Dane; Wang, Xudong<br><br># Supplementary Data Description<br>These files support the findings reported in the associated manuscript and provide comprehensive access to the image scoring pipeline, raw and processed data, as well as evaluation outputs used in the study. The dataset is organized into three main directories, along with several script and result files as described below.<br>## Folder Overview<br>### `raw_images/`<br>This directory contains the original, unprocessed grayscale images used as input for scoring. Each image is named according to its chemical element label (e.g., `Fe_0.png`, `Bi_2.png`). These images serve as the ground truth source material evaluated both by human experts and language models (LLMs).### `images_normalized/`This directory includes preprocessed versions of the raw images, created using the script `normalize.py`. The normalization ensures consistent contrast and intensity ranges across all images. The filenames mirror those in `raw_images/`.### `image_scoring/`This is the primary directory containing the full set of evaluation data and processing code. Key contents include:#### Scripts:- `normalize.py`: Script for generating normalized images from the raw inputs.<br>- `calculate_plot_icc_scores.py`: Calculates inter-rater reliability metrics (e.g., ICC scores) and produces visualizations.<br>- `llm_scorer.py`: Main script that runs image evaluation using large language models (LLMs) in conjunction with byte-encoded image input.<br>#### CSV Files:- `expert_data.csv`: Human expert scores for each image, used as a reference for evaluation.<br>- `gpt_data_all.csv`: Raw output scores from the LLM across all image prompts.<br>- `gpt_data_all.csv.results`: Post-processed summary statistics based on the GPT scores.<br>#### PDFs (Final Evaluation Plots):- `All_Experts.pdf`: Aggregated comparison plots across all expert scores.<br>- `Expert_AVG.pdf`: Visualization of the average expert scores per image.<br>- `gpt_data_all.csv_All_Experts.pdf`: Comparison between LLM and all expert scores.<br>- `gpt_data_all.csv_Expert_AVG.pdf`: Comparison between LLM and averaged expert scores.<br>#### Image-related Files:Each image has a set of associated metadata and interaction logs:- `.png`: Normalized image used in evaluation (same as in `images_normalized/`).<br>- `.png.conversation`: Full LLM conversation during evaluation of this image, including the byte-encoded input, and prompts and answers.<br>- `.png.fingerprint`: captures the model seed and prompt fingerprint for reproducibility.<br>- `.png.responses`: ChatCompletion object details for reproducibility.<br>Filenames follow a standardized format based on the element symbol and an image index (e.g., `p-Fe_0.png`).<br>## Notes on Naming and Prefixes<br>- `p-` prefix in image filenames denotes processed or prompt-ready versions of the raw images.<br>- The numerical suffix (e.g., `_0`, `_1`, `_2`) differentiates multiple samples for the same element.<br>- LLM interactions (`conversation`) and reproducibility data (`fingerprint`) are matched to specific image instances by name.<br>## Reproducibility and Usage<br>All LLM-based evaluations are reproducible using the `llm_scorer.py` script, together with the `fingerprint` files for each image, which include the random seed and parameters used. The `conversation` logs provide full transparency into the entire conversation.<br>