Reactivity-Informed Machine Learning for Performance Prediction and Design Space Exploration of Alkali-Activated Slag

• URL: http://arxiv.org/abs/2606.06765v1
• Date: Thu, 04 Jun 2026 23:04:54 GMT
• Title: Reactivity-Informed Machine Learning for Performance Prediction and Design Space Exploration of Alkali-Activated Slag
• Abstract summary: We curated the largest literature-derived alkali-activated slag dataset to date.<n>It includes over 3100 strength records, 155 chemically distinct ground granulated blast-furnace slags, and 24 attributes incorporating precursor chemistry, fineness, and reactivity.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Establishing quantitative relationships among mix design, raw material properties, curing conditions, and performance remains a long-standing challenge in cementitious materials, particularly for alkali-activated materials with variable precursor and activator chemistry. Here, we curated the largest literature-derived alkali-activated slag (AAS) dataset to date, comprising over 3100 compressive strength records, 155 chemically distinct ground granulated blast-furnace slags (GGBSs), and 24 attributes incorporating precursor chemistry, fineness, and reactivity. Multiple machine learning (ML) algorithms were benchmarked across progressively enriched feature scenarios, demonstrating that integrating GGBS compositions, fineness, curing conditions, and specimen geometry improves predictive performance. The average metal oxide dissociation energy (AMODE), a physically interpretable representation of precursor reactivity, provides a compact alternative descriptor to explicit oxide compositions while enabling comparable predictive performance. Model interpretation revealed physically consistent trends from heterogeneous data, including non-monotonic effects of Na2O dosage and silicate modulus, reduced predicted strength at higher water content and larger specimen size, and coupled oxide-level effects more coherently represented by AMODE than by individual oxide contents. Statistically constrained design space exploration reveals reactivity-dependent trade-offs among strength, embodied CO2 emissions, and cost. The design maps identify high-strength regions with substantially lower CO2 emissions than OPC-based references at similar cost. Overall, this work demonstrates how reactivity-informed ML can extract physically meaningful trends from heterogeneous AAS data and guide source-dependent binder design. The curated dataset is publicly accessible to support advances in cement and concrete research.

Related papers

• Chemical filters for ultra-high-throughput materials screening and generation [9.627197906011299]
  We introduce a chemical operator that recasts large chemical rules as an algorithmic prior for evaluating and guiding generative materials discovery.<n>Built on the open-source SMACT package, a data-informed oxidation-state model exposes tunable thresholds.<n>This work establishes a foundation for oxidation-state-aware generative models.
  arXiv  Detail & Related papers  (2026-07-20T13:06:54Z)
• ChemVA: Advancing Large Language Models on Chemical Reaction Diagrams Understanding [56.94419746470799]
  Large Language Models (LLMs) have revolutionized scientific text processing, but exhibit a significant capability gap when interpreting chemical reaction diagrams.<n>We propose the Chemical Visual Activation (ChemVA) framework, which employs a Visual Anchor mechanism to ground functional groups.<n>We evaluate our approach on OCRD-Bench, a newly constructed dataset featuring dense visual-semantic contexts.
  arXiv  Detail & Related papers  (2026-05-17T01:12:50Z)
• Gaussian Process Regression-based Knowledge Distillation Framework for Simultaneous Prediction of Physical and Mechanical Properties of Epoxy Polymers [0.0]
  We develop an informed Gaussian Process Regression-based Knowledge Distillation (GPR-KD) framework for predicting multiple physical and mechanical properties of thermoset epoxy polymers.<n>The framework combines GPR interpretability and robustness with deep learning scalability and generalization.
  arXiv  Detail & Related papers  (2026-03-12T12:59:03Z)
• Rep3Net: An Approach Exploiting Multimodal Representation for Molecular Bioactivity Prediction [0.8049701904919515]
  In early stage drug discovery, bioactivity prediction of molecules against target proteins plays a crucial role.<n>We propose Rep3Net, a unified deep learning architecture that not only incorporates descriptor data but also includes spatial and relational information.<n>Our model employing multimodald features produce reliable bioactivity prediction on Poly [ADP-ribose] polymerase 1 dataset.
  arXiv  Detail & Related papers  (2025-11-29T15:39:48Z)
• Enhancing Chemical Reaction and Retrosynthesis Prediction with Large Language Model and Dual-task Learning [8.402406301818905]
  Large language models (LLMs) have shown potential in many domains.<n>ChemDual is a novel framework for accurate chemical synthesis.<n>ChemDual achieves state-of-the-art performance in both predictions of reaction and retrosynthesis.
  arXiv  Detail & Related papers  (2025-05-05T13:31:36Z)
• SAGE-Amine: Generative Amine Design with Multi-Property Optimization for Efficient CO2 Capture [0.9968037829925945]
  SAGE-Amine is a generative modeling approach to design new amines tailored for CO2 capture.<n>It generates novel amines by leveraging autoregressive natural language processing models trained on amine datasets.<n>It successfully identified known amines for CO2 capture from scratch and successfully performed single-property optimization.
  arXiv  Detail & Related papers  (2025-03-04T12:02:36Z)
• Retrosynthesis prediction enhanced by in-silico reaction data augmentation [66.5643280109899]
  We present RetroWISE, a framework that employs a base model inferred from real paired data to perform in-silico reaction generation and augmentation. On three benchmark datasets, RetroWISE achieves the best overall performance against state-of-the-art models.
  arXiv  Detail & Related papers  (2024-01-31T07:40:37Z)
• 3DReact: Geometric deep learning for chemical reactions [35.38031930589095]
  We introduce 3DReact, a deep geometric learning model to predict reaction properties from three-dimensional structures of reactants and products. We show that, compared to existing models for reaction property prediction, 3DReact offers a flexible framework that exploits atom-mapping information. It performs systematically well across different datasets, atom-mapping regimes, as well as both geometries and extrapolation tasks.
  arXiv  Detail & Related papers  (2023-12-13T17:26:54Z)
• Towards out-of-distribution generalizable predictions of chemical kinetics properties [61.15970601264632]
  Out-Of-Distribution (OOD) kinetic property prediction is required to be generalizable. In this paper, we categorize the OOD kinetic property prediction into three levels (structure, condition, and mechanism) We create comprehensive datasets to benchmark the state-of-the-art ML approaches for reaction prediction in the OOD setting and the state-of-the-art graph OOD methods in kinetics property prediction problems.
  arXiv  Detail & Related papers  (2023-10-04T20:36:41Z)
• Multi-Task Mixture Density Graph Neural Networks for Predicting Cu-based Single-Atom Alloy Catalysts for CO2 Reduction Reaction [61.9212585617803]
  Graph neural networks (GNNs) have drawn more and more attention from material scientists. We develop a multi-task (MT) architecture based on DimeNet++ and mixture density networks to improve the performance of such task.
  arXiv  Detail & Related papers  (2022-09-15T13:52:15Z)
• Improving Molecular Representation Learning with Metric Learning-enhanced Optimal Transport [49.237577649802034]
  We develop a novel optimal transport-based algorithm termed MROT to enhance their generalization capability for molecular regression problems. MROT significantly outperforms state-of-the-art models, showing promising potential in accelerating the discovery of new substances.
  arXiv  Detail & Related papers  (2022-02-13T04:56:18Z)
• Unassisted Noise Reduction of Chemical Reaction Data Sets [59.127921057012564]
  We propose a machine learning-based, unassisted approach to remove chemically wrong entries from data sets. Our results show an improved prediction quality for models trained on the cleaned and balanced data sets.
  arXiv  Detail & Related papers  (2021-02-02T09:34:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.