The targeted removal of efficient but toxic corrosion inhibitors based on hexavalent chromium has provided an impetus for discovery of new, more benign organic compounds to fill that role. Developments in high-throughput synthesis of organic compounds, the establishment of large libraries of available chemicals, accelerated corrosion inhibition testing technologies, the increased capabilities of machine learning (ML) methods, and a better understanding of mechanisms of inhibition provide the potential to make discovery of new corrosion inhibitors faster and cheaper than ever before. These technical developments in the corrosion inhibition field are summarized herein. We describe how data-driven machine learning methods can generate models linking molecular properties to corrosion inhibition that can be used to predict the performance of materials not yet synthesized or tested. The literature on inhibition mechanisms is briefly summarized along with quantitative structure–property relationships models of small organic molecule corrosion inhibitors. The success of these methods provides a paradigm for the rapid discovery of novel, effective corrosion inhibitors for a range of metals and alloys, in diverse environments. A comprehensive list of corrosion inhibitors tested for various substrates that was curated as part of this review is accessible online https://excorr.web.app/database and available in a machine-readable format.

中文翻译：

---

抑制机制、自动化和计算模型对有机缓蚀剂发现的影响


基于六价铬的高效但有毒的缓蚀剂的靶向去除为发现新的、更温和的有机化合物来填补这一角色提供了动力。有机化合物高通量合成的发展、可用化学品的大型库的建立、加速腐蚀抑制测试技术、机器学习 （ML） 方法能力的增强以及对抑制机制的更好理解，使新型腐蚀抑制剂的发现比以往任何时候都更快、更便宜。本文总结了腐蚀抑制领域的这些技术发展。我们描述了数据驱动的机器学习方法如何生成将分子特性与腐蚀抑制联系起来的模型，这些模型可用于预测尚未合成或测试的材料的性能。简要总结了有关抑制机制的文献以及有机小分子缓蚀剂的定量结构-性质关系模型。这些方法的成功为在不同环境中快速发现适用于各种金属和合金的新型有效缓蚀剂提供了一种范例。作为本综述的一部分，针对各种基材进行测试的缓蚀剂的综合清单可在线 https://excorr.web.app/database 访问，并以机器可读的格式提供。