化学文献检索从论文级降到观点级
过去查化学文献，系统给你一堆论文，你得自己翻、自己拼。AskChem 把每篇论文拆成一条条带出处的原子化观点，检索单位从「论文」变成「观点」，每条都挂 DOI 和原文引文。它已索引 147K 篇论文、240 万条观点，还提供网页、API 和 AI 代理接口。实测中，让 AI 读文献时接入 AskChem，引用可解析率从 88.3% 提到 100%。做化学研究、写综述、或训练化学 AI 的人，这是能直接用的基础设施。
📄 原文摘要(英文)
Chemistry literature synthesis often requires assembling specific findings scattered across many publications, yet existing literature-search systems primarily return ranked document lists. As a result, scientists and AI agents need to locate relevant information, verify their provenance, and assemble cross-paper answers manually. We present AskChem, a claim-centered infrastructure for cross-paper chemistry search. AskChem changes the unit of retrieval from the paper to the provenance-carrying claim: each paper is converted into atomic, typed claims, each grounded by a source DOI and a verbatim quote or an explicit evidence locator. Over this shared claim store, AskChem exposes complementary structures for search and synthesis: a stabilized faceted taxonomy for hierarchical retrieval and browsing, an evidence graph linking claims through relations, and an exploratory living taxonomy that situates indexed papers under scientific principles. AskChem currently indexes 2.4M claims from 147K papers and provides a web interface, as well as REST, SDK, and MCP access for AI agents. On AskChem-Bench, grounding a GPT-5.5 reader in AskChem yields 100% resolvable DOIs, compared with 88.3% without retrieval, and the highest citation density among five tested systems. AskChem is live at https://askchem.org.