The molecular-scale mechanical properties of calcium silicate hydrates are crucial to the macro performance of cementitious materials, while achieving coincidence between accuracy and efficiency in computational simulations still remains a challenge. This study utilizes a deep-learning potential, specifically developed for calcium silicate hydrates based on artificial neural network, to achieve molecular dynamics simulations with accuracy comparable to first-principle methods. With this potential, the elastic properties and uniaxial mechanical behaviors are explored, wherein the anisotropy and impact mechanism of calcium ratios are analyzed. The results add to evidence that the deep-learning potential possess a higher accuracy than common force fields. The anisotropy of elastic modulus is mainly attributed to different atomic interactions in various directions, while the anisotropy of strength is additionally affected by the form of failure. This study may advance the accurate molecular-scale simulation and deepen the understanding of the strength source and cohesion mechanism of cement-based materials.

中文翻译：

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基于深度学习潜在分子动力学的硅酸钙水合物力学性质和失效机制研究


硅酸钙水合物的分子尺度力学性能对于胶凝材料的宏性能至关重要，而在计算模拟中实现精度和效率之间的一致性仍然是一个挑战。本研究利用了专为基于人工神经网络的硅酸钙水合物开发的深度学习潜力，实现了与第一性原理方法相当的分子动力学模拟。利用这种潜力，探索了弹性性能和单轴力学行为，其中分析了钙比的各向异性和影响机制。结果进一步证明，深度学习潜力比普通力场具有更高的准确性。弹性模量的各向异性主要归因于不同方向上的不同原子相互作用，而强度的各向异性还受到失效形式的影响。本研究可能推进精确的分子尺度模拟，并加深对水泥基材料强度来源和内聚机制的理解。