Directed Electrostatics Strategy Integrated as a Graph Neural Network Approach for Accelerated Cluster Structure Prediction.

IF 5.7 1区 化学 Q2 CHEMISTRY, PHYSICAL Journal of Chemical Theory and Computation Pub Date : 2025-01-28 Epub Date: 2025-01-15 DOI:10.1021/acs.jctc.4c01257
Sridatri Nandy, K V Jovan Jose
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Abstract

We present a directed electrostatics strategy integrated as a graph neural network (DESIGNN) approach for predicting stable nanocluster structures on their potential energy surfaces (PESs). The DESIGNN approach is a graph neural network (GNN)-based model for building structures of large atomic clusters with specific sizes and point-group symmetry. This model assists in the structure building of atomic metal clusters by predicting molecular electrostatic potential (MESP) topography minima on their structural evolution paths. The DESIGNN approach is benchmarked on the prototype Mgn clusters with n < 150. The predicted MESP topography minima of Mgn clusters, n < 70, fairly agrees with the whole-molecule MESP topography calculations. Moreover, the ground-state structures of Mgn (n = 4-32) clusters generated through the DESIGNN approach corroborate well with the global minimum structures reported in the literature. Furthermore, this approach could generate novel symmetric isomers of medium to large Mgn clusters in the size regime, n < 150, by constraining the point-group symmetry of the parent clusters. The parent growth potential (GP) of a cluster gives a measure of its parent cluster to accommodate more atoms and characterize the structures on the DESIGNN-guided path. The GP of a cluster can also be interpreted as a measure of the cooperative interaction relative to its parent cluster. Along the highest GP paths, the DESIGNN approach is further employed to generate stable Mgn nanoclusters with n = 228, 236, 257, 260. Therefore, the DESIGNN approach holds great promise in accelerating the structure search and prediction of large metal clusters guided through MESP topography.

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基于图神经网络的定向静电策略加速簇结构预测。
我们提出了一种定向静电策略,集成为图神经网络(DESIGNN)方法,用于预测其势能表面(PESs)上的稳定纳米团簇结构。DESIGNN方法是一种基于图神经网络(GNN)的模型,用于构建具有特定尺寸和点群对称性的大原子团簇结构。该模型通过预测金属原子团簇结构演化路径上的分子静电势(MESP)形貌极小值,帮助构建金属原子团簇的结构。DESIGNN方法在n < 150的原型Mgn集群上进行基准测试。预测的Mgn簇的MESP形貌最小值n < 70,与整个分子的MESP形貌计算结果相当一致。此外,通过DESIGNN方法生成的Mgn (n = 4-32)簇的基态结构与文献中报道的全局最小结构相吻合。此外,该方法可以通过限制母团簇的点群对称性,在n < 150的尺寸范围内生成中大型Mgn团簇的新型对称异构体。簇的母体生长势(GP)给出了其母体簇容纳更多原子的度量,并表征了设计引导路径上的结构。集群的GP也可以解释为相对于其父集群的协作交互的度量。沿着最高GP路径,进一步采用DESIGNN方法生成n = 228、236、257、260的稳定Mgn纳米团簇。因此,DESIGNN方法在加速通过MESP地形引导的大型金属团簇的结构搜索和预测方面具有很大的前景。
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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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