一个多世纪以来，"十八电子规则"始终是金属有机化学领域的金科玉律。然而，日本冲绳科学技术研究 所联合德俄科研团队在7日的《自然·通讯》杂志上发表论文称，他们合成出首个拥有20个电子的稳定二 茂铁衍生物。这项突破有望为化学研究带来新的可能性，并催生新型催化剂。 作为过渡金属配合物稳定性的黄金标准，"十八电子规则"指出：当金属中心电子数与配体贡献电子数之 和达到18时，体系最稳定。这一原理支撑着催化科学与材料领域的诸多重大发现。 1951年问世的二茂铁，正是诠释这一规则的经典范例。二茂铁不仅颠覆了人们对金属—有机键的认知， 开创了现代有机金属化学研究领域，更使其发现者摘得1973年诺贝尔化学奖桂冠。这种"明星分子"激励 着科学家对金属—有机化合物的探索向纵深推进。 团队表示，这为化学家打开了一扇新的窗口。新型化合物中铁—氮键的形成，使得电子转移途径更丰富 多样。这种特性有望让这种新分子在能源储存、化学合成等领域大显身手，无论是作为高效催化剂，还 是功能材料，都可能给人们带来惊喜。 目前，二茂铁及其衍生物家族成员已遍及太阳能电池、医药制剂、医疗器械等诸多领域。而这项突破性 研究，不仅为现有应用开辟了优化空间，更可能 ...

Core Insights - The integration of artificial intelligence (AI) and automation in synthetic chemistry is seen as the future, enhancing efficiency and reducing reliance on traditional trial-and-error methods [1][3][4] - The vastness of chemical space presents significant challenges for chemists, with the theoretical number of small molecules that can be synthesized reaching 10^60, far exceeding the number of stars in the universe [2][3] - Current methodologies in synthetic chemistry include "top-down" experimental approaches and "bottom-up" theoretical approaches, both facing efficiency and universality challenges, necessitating new tools [3][4] Group 1: Challenges in Synthetic Chemistry - Synthetic chemistry is fundamental for creating materials essential for agriculture, health, and industry, but faces increasing demands for new materials and performance [1][2] - The "top-down" approach relies on chemists' intuition and experience, while the "bottom-up" approach uses computational methods, both of which have limitations in efficiency and applicability [2][3] Group 2: Automation and AI in Research - Automation in laboratories, such as high-throughput technology, has been adopted to enhance efficiency in catalyst development, significantly reducing the time required for experiments [4][5] - The use of automated platforms allows researchers to design and test thousands of catalyst formulations quickly, leading to the discovery of new materials that would take much longer through traditional methods [5][6] Group 3: Future Directions - AI's role in chemistry is currently as a supportive tool rather than a replacement for human intuition, with significant potential for development in interpreting experimental results [6][8] - The concept of "self-driving laboratories" is emerging, where automated systems can analyze results and autonomously design subsequent experiments, creating a rapid iterative cycle of design, execution, and learning [9][10]