Exploring the Polymorphism of Dicalcium Silicates Using Transfer Learning Enhanced Machine Learning Atomic Potentials.

IF 5.7 1区 化学 Q2 CHEMISTRY, PHYSICAL
Journal of Chemical Theory and Computation Pub Date : 2024-09-10 Epub Date: 2024-08-22 DOI:10.1021/acs.jctc.4c00479
Jon López-Zorrilla, Xabier M Aretxabaleta, Hegoi Manzano
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引用次数: 0

Abstract

Belitic cements are a greener alternative to Ordinary Portland Cement due to the lower CO2 associated with their production. However, their low reactivity with water is currently a drawback, resulting in longer setting times. In this study, we utilize a combination of evolutionary algorithms and machine learning atomic potentials (MLPs) to identify previously unreported belite polymorphs that may exhibit higher hydraulic reactivity than the known phases. To address the high computational demand of this methodology, we propose a novel transfer learning approach for generating MLPs. First, the models are pretrained on a large set of classical data (ReaxFF) and then retrained with density functional theory (DFT) data. We demonstrate that the transfer learning enhanced potentials exhibit higher accuracy, require less training data, and are more transferable than those trained exclusively on DFT data. The generated machine learning potential enables a fast, exhaustive, and reliable exploration of the dicalcium silicate polymorphs. This includes studying their stability through phonon analysis and calculating their structural and elastic properties. Overall, we identify ten new belite polymorphs within the energy range of the existing ones, including a layered phase with potentially high reactivity.

Abstract Image

利用迁移学习增强型机器学习原子势探索硅酸二钙的多态性。
贝利特水泥是普通波特兰水泥的绿色替代品,因为其生产过程中产生的二氧化碳较少。然而,它们与水的低反应性是目前的一个缺点,导致凝结时间较长。在这项研究中,我们将进化算法与机器学习原子势能(MLP)相结合,识别出以前未报道过的白云石多晶体,这些多晶体可能会表现出比已知物相更高的水反应性。为了解决这种方法的高计算需求,我们提出了一种新颖的迁移学习方法来生成 MLP。首先,在大量经典数据集(ReaxFF)上对模型进行预训练,然后用密度泛函理论(DFT)数据对模型进行再训练。我们证明,迁移学习增强型势能表现出更高的准确性,所需的训练数据更少,而且比完全基于 DFT 数据训练的势能更具可迁移性。生成的机器学习势能对硅酸钙多晶体进行快速、详尽和可靠的探索。这包括通过声子分析研究它们的稳定性,以及计算它们的结构和弹性特性。总之,我们在现有褐铁矿多晶体的能量范围内发现了十种新的褐铁矿多晶体,包括一种具有潜在高反应性的层状相。
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来源期刊
Journal of Chemical Theory and Computation
Journal of Chemical Theory and Computation 化学-物理:原子、分子和化学物理
CiteScore
9.90
自引率
16.40%
发文量
568
审稿时长
1 months
期刊介绍: The Journal of Chemical Theory and Computation invites new and original contributions with the understanding that, if accepted, they will not be published elsewhere. Papers reporting new theories, methodology, and/or important applications in quantum electronic structure, molecular dynamics, and statistical mechanics are appropriate for submission to this Journal. Specific topics include advances in or applications of ab initio quantum mechanics, density functional theory, design and properties of new materials, surface science, Monte Carlo simulations, solvation models, QM/MM calculations, biomolecular structure prediction, and molecular dynamics in the broadest sense including gas-phase dynamics, ab initio dynamics, biomolecular dynamics, and protein folding. The Journal does not consider papers that are straightforward applications of known methods including DFT and molecular dynamics. The Journal favors submissions that include advances in theory or methodology with applications to compelling problems.
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