化学论文里找结论太费劲？AskChem 把 14.7 万篇论文拆成带引用的声明，直接搜观点还能看来源，AI 也能通过接口调。
AskChem 将论文拆解为带来源 DOI 和原文引用的原子声明。目前已索引来自 14.7 万篇论文的 240 万条声明，提供网页、REST、SDK 和 MCP 接口。在 AskChem-Bench 基准上，接入 GPT-5.5 阅读器后 DOI 可解析率达到 100%，未检索时为 88.3%。系统还提供证据图和分类检索，支持跨论文验证信息。
AskChem: Claim-Centered Infrastructure for Chemistry Literature Synthesis
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.