Analysis of Self-Assembly Pathways with Unsupervised Machine Learning Algorithms
Analysis of Self-Assembly Pathways with Unsupervised Machine Learning Algorithms
复制标题
DOI：
10.1021/acs.jpcb.9b09621
复制
发表时间：
2020-01-09
影响因子：
3.3
通讯作者：
Glotzer, Sharon C.
中科院分区：
文献类型：
--
作者：
Adorf, Carl S.;Moore, Timothy C.;Glotzer, Sharon C.
胶体和纳米粒子系统显示出丰富和令人兴奋的相行为，包括高度复杂的晶体结构的自组装。结晶的成核和生长途径已经在计算和实验上进行了研究，但是形成临界前核和随后的晶体生长的机制还没有完全理解。应用于多粒子系统的机器学习算法的最新进展已经导致相变的高通量分析和晶体结构识别能力的重大突破。我们建立在这些技术，以确定和分析途径的成核和生长在过冷液体的胶体系统建模与各向同性对电位。我们的研究涉及开发在基于球面谐波的描述符上训练的无监督机器学习模型。这些模型使我们能够确定集群的本地环境，目前之前和结晶过程中。我们分析这些环境，以确定流行的图案和当地秩序内的过冷液体形成之前的临界核。
Colloidal and nanoparticle systems display a rich and exciting phase behavior including the self-assembly of highly complex crystal structures. Nucleation and growth pathways toward crystallization have been studied both computationally and experimentally, but the mechanisms for the formation of the precritical nucleus and consequent crystal growth are yet to be fully understood. Recent advances in the application of machine learning algorithms applied to many-particle systems have led to significant breakthroughs in the ability for high-throughput analysis of phase transitions and the identification of crystal structures. We build upon these techniques to identify and analyze pathways for nucleation and growth in supercooled liquids of colloidal systems modeled with isotropic pair potentials. Our study involves the development of unsupervised machine learning models trained on spherical-harmonics-based descriptors. These models allow us to determine clusters of local environments that are present prior to and during crystallization. We analyze these environments to identify prevalent motifs and local order within the supercooled liquid prior to formation of the critical nucleus.