Article(id=1320123016516841693, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1320122987781673454, articleNumber=null, orderNo=null, doi=10.14062/j.issn.0454-5648.20260261, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1776787200000, receivedDateStr=2026-04-22, revisedDate=1778342400000, revisedDateStr=2026-05-10, acceptedDate=null, acceptedDateStr=null, onlineDate=1792671453837, onlineDateStr=2026-10-22, pubDate=1784044800000, pubDateStr=2026-07-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792671453837, onlineIssueDateStr=2026-10-22, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792671453837, creator=13701087609, updateTime=1792671453837, updator=13701087609, issue=Issue{id=1320122987781673454, tenantId=1146029695717560320, journalId=1263187385517883426, year='2026', volume='54', issue='10', pageStart='3315', pageEnd='3812', issueExtLink='null', onlineDate='null', pubDate='1792425600000', pubDateStr='2026-10-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1792671446986, creator='13701087609', updateTime=1792720523418, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1320328829357375869, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1320122987781673454, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1320328829357375870, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1320122987781673454, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=3398, endPage=3413, ext={EN=ArticleExt(id=1320123016709779678, articleId=1320123016516841693, tenantId=1146029695717560320, journalId=1263187385517883426, language=EN, title=Constraint-Driven Optimization and Screening of Low-Carbon Clinker Based on a High-Throughput Process and Machine Learning Models, columnId=1320122989312586697, journalTitle=Journal of the Chinese Ceramic Society, columnName=Special Issue on Key Inorganic Non-metallic Materials for Intelligent Construction at the 28th Annual Meeting of the China Association for Science and Technology–AI-Driven Innovation in Cement-Based Materials-Research Articles, runingTitle=null, highlight=null, articleAbstract=
Introduction Conventional development of low-carbon clinker mainly relies on empirical or mechanistic understanding of high-temperature and hydration reactions among clinker phases. This strategy, which combines experimental trial-and-error with mechanistic analysis, is generally time-consuming. Therefore, conventional development approaches are no longer sufficient to meet the demand for the rapid development of novel low-carbon clinkers. The development of high-throughput (HT) methods and machine learning (ML) has provided a new paradigm for the design of cement-based materials. However, available clinker data for ML-based clinker design are usually collected from scattered sources, and they differ substantially in preparation procedures, testing conditions, and compositional definitions, making them difficult to use directly. To address these issues, this study used clinker phases as compositional units and constructed a clinker dataset through a standardized HT preparation procedure. On this basis, the predictive performance of different ML models for the 28 d compressive strength of clinker was compared. Furthermore, the optimized surrogate model was coupled with a genetic algorithm to screen for low-carbon clinkers under constraints on 28 d compressive strength and CO2 emissions. This approach enabled evaluation of the applicability of the proposed data-driven framework for the design of novel low-carbon clinkers.
Methods In this study, 11 clinker phases were selected as the basis for compositional design, and each clinker composition prepared by dry-mixing these phases with gypsum in specified proportions was regarded as one specimen. A clinker dataset was constructed using an HT experimental procedure. The dataset contained information on phase composition, water-to-binder ratio (W/B), 3 and 28 d compressive strengths for a total of 264 specimens. Among them, 228 specimens were used for training and validation, while 36 specimens were reserved as a test set. The test set was further divided into in-system and out-of-system specimens to evaluate model stability. For model construction, five nonlinear ML algorithms were employed. The normalized phase composition after gypsum incorporation was used as the input. The 28 d compressive strength was used as the output. Model hyperparameters were determined by Bayesian optimization, and repeated constrained holdout validation was adopted to reduce the influence of random splitting on model evaluation. Model performance was evaluated using mean squared error, root mean squared error (RMSE), mean absolute error, and mean absolute percentage error. After the surrogate model had been determined, it was coupled with a genetic algorithm to search the phase composition space for low-carbon clinkers with a predicted 28 d compressive strength higher than 42.5 MPa and CO2 emissions lower than 671 or 592 kg CO2/t. These CO2-emission thresholds were reduced by 15% and 25%, respectively, relative to Portland clinker.
Results and discussion Dataset analysis showed that the specimens in the dataset exhibited pronounced compositional dispersion and system-level heterogeneity. This heterogeneity was one of the main sources of model instability. The W/B gradually increased from the C3S/C2S-rich region toward regions enriched in aluminate/ferrite phases and C4A3$/C5S2$. The 3 d and 28 d compressive strengths of the training/validation set covered a range of 0-70 MPa, whereas the test set was mainly concentrated in the medium-to-high strength range. Some specimens exhibited strength regression from 3 d to 28 d. These complex strength-development patterns increased the difficulty of model learning but also provided useful information for identifying nonlinear relationships between composition and compressive strength. Validation results showed that, under the conditions of limited data and high compositional heterogeneity, controlled model capacity and moderate regularization were more beneficial for improving prediction stability. The optimized hyperparameters of different models all reflected a tendency to suppress overfitting. Among the models, the artificial neural network (ANN) adopted a relatively small architecture with regularization and achieved the best performance on the validation sets. The test-set results further demonstrated that the ANN had better cross-system generalization capability. Compared with the other models, the ANN achieved an overall RMSE of 3.9 MPa on the test set. Subsequently, the ANN was selected as the surrogate model and coupled with a genetic algorithm for the inverse design of low-carbon clinkers. When the CO2-emission reduction constraint was tightened to 25%, the genetic algorithm further favored low-carbon phases and generated candidate clinkers dominated by aluminate phases or the C4A3$ phase, with CO2 emissions of approximately 555 kg CO2/t. Experimental validation showed that the measured 28 d compressive strengths of the candidate clinker compositions were close to the model predictions. The prediction errors for all specimens were lower than 10% of the measured strengths.
Conclusions This study constructs a clinker dataset based on a HT preparation procedure and establishes an ML prediction and constrained-optimization framework for screening low-carbon clinkers. The results show that the dataset is characterized by broad compositional coverage, local sparsity, and pronounced system heterogeneity. Therefore, the model needs to balance nonlinear representation capability with regularization constraints. Comparisons on the validation sets and the test set show that the low-capacity ANN with moderate regularization achieves the best performance in terms of prediction accuracy. Its RMSE is 3.9 MPa on the overall test set, and the RMSE values for the in-system and out-of-system subsets are 3.1 MPa and 4.2 MPa, respectively, indicating that it can serve as the surrogate model for inverse design of low-carbon clinker. XGBoost can be regarded as a competitive potential surrogate model, but it tends to produce relatively large prediction errors for some specimens. By coupling the ANN surrogate model with a genetic algorithm, four candidate low-carbon clinkers satisfying the 28 d compressive strength and CO2 emission constraints are screened. Experimental validation confirms good agreement between the predicted and measured compressive strengths. These results demonstrate that the proposed dataset construction and surrogate model screening strategy can be used to explore of novel low-carbon clinkers.
, authors=Munan ZHAI, Xiao ZHI, Jiayuan YE, Xuehong REN, Wensheng ZHANG, authorsList=Munan ZHAI, Xiao ZHI, Jiayuan YE, Xuehong REN, Wensheng ZHANG, authorCompany=null, correspAuthors=Jiayuan YE, authorNote=
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水泥熟料涉及复杂的高温固/液相反应和矿相间协同水化过程，且不同实验流程导致文献数据在结构化程度和可比性方面存在差异，这限制了机器学习算法在新型熟料设计中的应用与发展。本工作将熟料单矿视为最小组成单元，通过标准化高通量实验构建了基于熟料组成的数据集，共包含264组样本。采用贝叶斯优化方法对5种非线性模型进行超参数寻优，比较不同类别算法在所构建数据集上的预测精度，并利用预先设计的测试集，严格区分体系内、外两类预测情形。结果表明，具有适度正则化的低容量人工神经网络模型表现出更高的预测精度，且对体系外样本具有更强的鲁棒性。此外，在28 d抗压强度与碳排放约束阈值条件下，利用优选的代理模型与遗传算法耦合，筛选得到4组满足约束条件的低碳熟料组成。经实验验证，4组低碳熟料28 d抗压强度结果与模型预测结果基本一致，表明所构建的高通量实验与代理模型可用于低碳熟料的初步筛选。
, authors=翟牧楠, 郅晓, 叶家元, 任雪红, 张文生, authorsList=翟牧楠, 郅晓, 叶家元, 任雪红, 张文生, authorCompany=null, correspAuthors=叶家元, authorNote=
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叶家元（1980-），男，博士，正高级工程师。
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