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，你得自己翻找哪篇说了什么、再拼起来。AskChem 把检索单位从「论文」改成「带出处的观点」:每篇论文被拆成一条条原子化、带类型的陈述，每条都挂着一个 DOI 和一句原文佐证。目前它已索引 147K 篇论文、240 万条观点，并提供网页、API 和给 AI 代理用的接口。实测中，让 AI 读文献时接入 AskChem，能找到的引用 DOI 从 88.3% 提到 100%，引用密度也是五个系统里最高的。它不是你明天写论文就能直接用的工具，但它指向一个趋势：文献检索正在从「给你一堆文件」变成「直接给你可核验的事实」。
📄 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.