Two-dimensional (2D) materials have shown broad application prospects in fields such as energy, environment, and aerospace owing to their unique electrical, mechanical, thermal, and other properties. With the development of artificial intelligence (AI), the discovery and design of novel 2D materials have been significantly accelerated. However, due to the lack of basic theories of material synthesis, identifying reliable synthesis processes for theoretically designed materials is a challenge. The emergence of large language model offers approaches for the reliability prediction of material synthesis processes. However, its development is limited by the lack of publicly available data sets of material synthesis processes. To address this, we present the Material Synthesis 2025 (MatSyn25), a large-scale open data set of 2D material synthesis processes. MatSyn25 contains 163,240 pieces of synthesis process information extracted from 85,160 high-quality research articles, each including basic material information and detailed synthesis process steps. Based on MatSyn25, we developed MatSyn AI, which specializes in material synthesis, and provided an interactive web platform that enables multifaceted exploration of the data set (https://matsynai.stpaper.cn/). MatSyn25 is publicly available, allowing the research community to build upon our work and further advance AI-assisted materials science.
Li et al. (Thu,) studied this question.