Modified Activation-Relaxation Technique (ARTn) Method Tuned for Efficient Identification of Transition States in Surface Reactions,Journal of Chemical Theory and Computation

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Modified Activation-Relaxation Technique (ARTn) Method Tuned for Efficient Identification of Transition States in Surface Reactions
Journal of Chemical Theory and Computation ( IF 5.7 ) Pub Date : 2024-09-06 , DOI: 10.1021/acs.jctc.4c00767
Jisu Jung ₁ , Hyungmin An ₁ , Jinhee Lee ₂ , Seungwu Han _{1,

3}

Affiliation

Exploring potential energy surfaces (PES) is essential for unraveling the underlying mechanisms of chemical reactions and material properties. While the activation-relaxation technique (ARTn) is a state-of-the-art method for identifying saddle points on PES, it often faces challenges in complex energy landscapes, especially on surfaces. In this study, we introduce iso-ARTn, an enhanced ARTn method that incorporates constraints on an orthogonal hyperplane and employs an adaptive active volume. By leveraging a neural network potential (NNP) to conduct an exhaustive saddle point search on the Pt(111) surface with 0.3 monolayers of surface oxygen coverage, iso-ARTn achieves a success rate that is 8.2% higher than the original ARTn, with 40% fewer force calls. Moreover, this method effectively finds various saddle points without compromising the success rate. Combined with kinetic Monte Carlo simulations for event table construction, iso-ARTn with NNP demonstrates the capability to reveal structures consistent with experimental observations. This work signifies a substantial advancement in the investigation of PES, enhancing both the efficiency and breadth of saddle point searches.

中文翻译：

改进的活化弛豫技术 (ARTn) 方法可有效识别表面反应中的过渡态

探索势能面（PES）对于揭示化学反应和材料特性的潜在机制至关重要。虽然激活弛豫技术 (ARTn) 是识别 PES 鞍点的最先进方法，但它经常面临复杂能量景观的挑战，尤其是在表面上。在本研究中，我们引入了 iso-ARTn，这是一种增强的 ARTn 方法，它结合了正交超平面的约束并采用自适应活动体积。通过利用神经网络势 (NNP) 在表面氧覆盖度为 0.3 单层的 Pt(111) 表面上进行详尽的鞍点搜索，iso-ARTn 的成功率比原始 ARTn 高出 8.2%，其中 40强制呼叫减少 %。此外，该方法可以有效地找到各种鞍点，而不会影响成功率。结合用于事件表构建的动力学蒙特卡罗模拟，iso-ARTn 与 NNP 展示了揭示与实验观察结果一致的结构的能力。这项工作标志着 PES 研究的重大进展，提高了鞍点搜索的效率和广度。

更新日期：2024-09-06

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