GraphVAMPnets for uncovering slow collective variables of self-assembly dynamics.
GraphVAMPnets for uncovering slow collective variables of self-assembly dynamics.
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DOI：
10.1063/5.0158903
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发表时间：
2023-09
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通讯作者：
Bojun Liu;Mingyi Xue;Yunrui Qiu;K. Konovalov;Michael S O'Connor;Xuhui Huang
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作者：
Bojun Liu;Mingyi Xue;Yunrui Qiu;K. Konovalov;Michael S O'Connor;Xuhui Huang
揭示自组装动力学的慢集体变量（CV）对于阐明其众多的动力学组装途径并通过自下而上的方法驱动先进材料的新型结构的设计非常重要。然而，识别用于自组装的CV提出了若干挑战。首先，自组装系统通常由相同的单体组成，并且特征表示应该对排列和旋转对称性不变。物理坐标（如聚合大小）缺乏高分辨率的细节，而常见的几何坐标（如成对距离）则受到排列和旋转对称性挑战的阻碍。其次，自组装通常是一个下坡过程，并且轨迹通常遭受与自组装结构的解离相对应的向后转变的不充分采样。流行的降维方法，如时间-结构独立成分分析，施加详细的平衡约束，潜在地模糊了自组装的真实动力学。在这项工作中，我们采用GraphVAMPnets，它结合了图神经网络与马尔可夫过程（VAMP）理论的变分方法来识别自组装过程的慢CV。首先，GraphVAMPnets具有图神经网络的优点，其中图嵌入可以以高分辨率表示自组装结构，同时对排列和旋转对称性保持不变。其次，它建立在VAMP理论的基础上，该理论研究马尔可夫过程，而不强制详细的平衡约束，这解决了自组装过程中的平衡挑战。我们展示了GraphVAMPnets用于识别两个系统中自组装动力学的慢CV：两个疏水分子的聚集和片状颗粒的自组装。我们希望我们的GraphVAMPnets可以广泛应用于分子自组装。
Uncovering slow collective variables (CVs) of self-assembly dynamics is important to elucidate its numerous kinetic assembly pathways and drive the design of novel structures for advanced materials through the bottom-up approach. However, identifying the CVs for self-assembly presents several challenges. First, self-assembly systems often consist of identical monomers, and the feature representations should be invariant to permutations and rotational symmetries. Physical coordinates, such as aggregate size, lack high-resolution detail, while common geometric coordinates like pairwise distances are hindered by the permutation and rotational symmetry challenges. Second, self-assembly is usually a downhill process, and the trajectories often suffer from insufficient sampling of backward transitions that correspond to the dissociation of self-assembled structures. Popular dimensionality reduction methods, such as time-structure independent component analysis, impose detailed balance constraints, potentially obscuring the true dynamics of self-assembly. In this work, we employ GraphVAMPnets, which combines graph neural networks with a variational approach for Markovian process (VAMP) theory to identify the slow CVs of the self-assembly processes. First, GraphVAMPnets bears the advantages of graph neural networks, in which the graph embeddings can represent self-assembly structures in high-resolution while being invariant to permutations and rotational symmetries. Second, it is built upon VAMP theory, which studies Markov processes without forcing detailed balance constraints, which addresses the out-of-equilibrium challenge in the self-assembly process. We demonstrate GraphVAMPnets for identifying slow CVs of self-assembly kinetics in two systems: the aggregation of two hydrophobic molecules and the self-assembly of patchy particles. We expect that our GraphVAMPnets can be widely applied to molecular self-assembly.