The rapid advancement in artificial intelligence and natural language
processing has led to the development of large-scale datasets aimed at
benchmarking the performance of machine learning models. Herein, we introduce
'RetChemQA,' a comprehensive benchmark dataset designed to evaluate the
capabilities of such models in the domain of reticular chemistry. This dataset
includes both single-hop and multi-hop question-answer pairs, encompassing
approximately 45,000 Q&As for each type. The questions have been extracted from
an extensive corpus of literature containing about 2,530 research papers from
publishers including NAS, ACS, RSC, Elsevier, and Nature Publishing Group,
among others. The dataset has been generated using OpenAI's GPT-4 Turbo, a
cutting-edge model known for its exceptional language understanding and
generation capabilities. In addition to the Q&A dataset, we also release a
dataset of synthesis conditions extracted from the corpus of literature used in
this study. The aim of RetChemQA is to provide a robust platform for the
development and evaluation of advanced machine learning algorithms,
particularly for the reticular chemistry community. The dataset is structured
to reflect the complexities and nuances of real-world scientific discourse,
thereby enabling nuanced performance assessments across a variety of tasks. The
dataset is available at the following link:
this https URL

人工智能和自然语言处理的快速发展导致了大规模数据集的生成，该研究旨在评估机器学习模型在网状化学领域中的能力。RetChemQA 是一种全面的基准数据集，包括单跳和多跳问题 - 回答对，共约 45,000 个每种类型。该数据集使用 OpenAI 的 GPT-4 Turbo 生成，旨在为网状化学社区的高级机器学习算法的开发和评估提供强大的平台。

用于网状化学的单跳和多跳问答数据集的 GPT-4-Turbo

Single and Multi-Hop Question-Answering Datasets for Reticular Chemistry  with GPT-4-Turbo

We present a new framework integrating the AI model GPT-4 into the iterative
process of reticular chemistry experimentation, leveraging a cooperative
workflow of interaction between AI and a human apprentice. This GPT-4 Reticular
Chemist is an integrated system composed of three phases. Each of these
utilizes GPT-4 in various capacities, wherein GPT-4 provides detailed
instructions for chemical experimentation and the apprentice provides feedback
on the experimental outcomes, including both success and failures, for the
in-text learning of AI in the next iteration. This iterative human-AI
interaction enabled GPT-4 to learn from the outcomes, much like an experienced
chemist, by a prompt-learning strategy. Importantly, the system is based on
natural language for both development and operation, eliminating the need for
coding skills, and thus, make it accessible to all chemists. Our GPT-4
Reticular Chemist demonstrated the discovery of an isoreticular series of
metal-organic frameworks (MOFs), each of which was made using distinct
synthesis strategies and optimal conditions. This workflow presents a potential
for broader applications in scientific research by harnessing the capability of
large language models like GPT-4 to enhance the feasibility and efficiency of
research activities.

本研究基于人 - AI 交互合作，整合了 AI 模型 GPT-4 到网状化学实验的迭代过程中，通过自然语言开发和操作实现。使用 GPT-4 提供的详细指导进行实验，与学徒提供实验结果反馈进行文本学习，从而实现像有经验的化学家那样的快速学习策略。该工作流程在合成多种优化条件的金属有机骨架时表现出潜在的广泛应用前景。