【摘要】 基于元素掺杂的水化硅酸钙(C-S-H)凝胶分子强化可为水泥混凝土性能的提升提供新思路。当前，无论是试验还是模拟均局限于试错法的范畴，无法实现海量元素掺杂替代的系统寻优。本研究提出了一种基于机器学习的C-S-H分子元素掺杂高通量筛选方法，利用既有硅相黏土矿物数据预测不同化学组成的C-S-H分子性能，以分子结构形成能为指标对比甄别高稳定性分子结构，实现了多元素掺杂方案的批量化、高效化筛选，在8核计算力下，单位样本计算耗时小于1 s,且免于复杂建模流程。随机森林算法在分子元素掺杂高通量筛选案例中表现优异，测试样本的均方根误差RMSE为0.060,R~2为0.980。预测结果印证了电荷不守恒引起的晶体结构稳定性下降、高聚合度分子形成能降低以及非桥接氧质子化导致的化学作用弱化效应。本研究探究了Al、Fe、Ti、Na、Mg、Li、Cr、H等单元素和多元素掺杂方案，提出了Na/Al掺杂提升C-S-H稳定性的可行办法，印证了镁离子主动替代C-S-H中钙离子的动力学行为。该方法以第一性原理为底层理论框架，具有推广至无机非金属矿物体系的潜力，可实现对海量未知材料的高通量预测和筛选。更多还原

【Abstract】 Molecular reinforcement of calcium silicate hydrate(C-S-H) gel through elemental doping offers a new strategy for achieving improvements in the performance of cement-based concrete. Current experimental and simulation approaches remain confined to trial-and-error methodologies, lacking the capacity for systematic optimization across vast elemental doping schemes. This study proposed a machine learning-based high-throughput screening method for elemental doping in C-S-H molecules. The innovative approach leveraged existing phyllosilicate mineral data to predict the properties of C-S-H with varying chemical composition, using molecular formation energy as an indicator to identify highly stable molecular structures. This enables batchwise and efficient screening of multi-element doping configurations, with computational costs below 1 s per sample on an 8-core processor while bypassing complex modeling procedures. The random forest algorithm demonstrates exceptional performance in high-throughput screening of elemental doping cases, achieving an root mean square error(RMSE) of 0.060 and R~2 of 0.980 on test samples. Prediction results confirm: 1) stability reduction due to charge imbalance, 2) decreased formation energy in highly polymerized molecules, 3) weakened chemical effects from protonation of non-bridging oxygen sites. The study investigates mono-and multi-element doping schemes involving Al, Fe, Ti, Na, Mg, Li, Cr, and H, proposing feasible Na/Al co-doping approaches to enhance C-S-H stability, and validating magnesium ions’ kinetic propensity to substitute calcium sites in C-S-H. Grounded in first-principles theoretical frameworks, this method demonstrates potential for extension to inorganic non-metallic mineral systems, enabling high-throughput prediction and screening of numerous unknown materials.更多还原