arXiv cs.AI· Yevgeny Rakita, Paul Todd, James R. Neilson, Simon J. L. Billinge·· 3 天前AI 评分22
用 Provenance Action Graphs 加速材料合成：潜力、结构与演示
Complex materials engineering via Provenance Action Graphs: potential, structure and demonstration
AI 导读
研究提出用反应图（Provenance Action Graphs）建模材料合成，将反应步骤作为动作、产物作为观测，借助学习算法从多维合成路径中编码规则，并支持正向预测与逆向学习两种模式。工作聚焦固态合成中动力学主导的反应场景，目标是构建可由数据智能体共享和贡献的全局知识库，加速材料发现。
正文 · 原文
Abstract:This work discusses and tests the potential of reaction graphs for material synthesis, in which events consist of reaction steps (actions) and their outputs (observations). The importance comes from the requirement to synthesize technologically useful materials, especially those that are kinetically stabilized. Often, it is a science that follows years of trial and error, culminating in (published) successful recipes, but is truly driven by far more (unpublished) failures. The entry into the era of AI provides an opportunity to use learning algorithms to encode rules from multi-dimensional synthesis pathways, regardless of their technological usefulness, and to decode both forward prediction and inverse learning modes. The main incentive is to accelerate material discovery by generating a global knowledge base that can be shared among data agents and contributed to by them. We will focus on several scenarios in the common synthesis process during solid-state synthesis, primarily because these reactions are kinetic in nature and require closer monitoring and control of the reaction history.
| Comments: | 13 pages, 8 figures |
| Subjects: | Artificial Intelligence (cs.AI); Materials Science (cond-mat.mtrl-sci) |
| Cite as: | arXiv:2610.18314 [cs.AI] |
| (or arXiv:2610.18314v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.18314 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Simon Billinge [view email]
[v1]
Tue, 13 Oct 2026 12:57:31 UTC (4,036 KB)
来源：arXiv cs.AI · arxiv.org