Unsupervised Data-Driven Reconstruction of Molecular Motifs in Simple to Complex Dynamic Micelles

Abstract

The reshuffling mobility of molecular building blocks in self-assembled micelles is a key determinant of many their interesting properties, from emerging morphologies and surface compartmentalization, to dynamic reconfigurability and stimuli-responsiveness. However, the microscopic details of such complex structural dynamics are typically nontrivial to elucidate, especially in multicomponent assemblies. Here we show a machine-learning approach that allows us to reconstruct the structural and dynamic complexity of mono- and bicomponent surfactant micelles from high-dimensional data extracted from equilibrium molecular dynamics simulations. Unsupervised clustering of smooth overlap of atomic position (SOAP) data enables us to identify, in a set of multicomponent surfactant micelles, the dominant local molecular environments that emerge within them and to retrace their dynamics, in terms of exchange probabilities and transition pathways of the constituent building blocks. Tested on a variety of micelles differing in size and in the chemical nature of the constitutive self-assembling units, this approach effectively recognizes the molecular motifs populating them in an exquisitely agnostic and unsupervised way, and allows correlating them to their composition in terms of constitutive surfactant species.

Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling

“…In this work, we analyze molecular dynamics (MD) trajectories of various molecular/atomic systems, from soft to crystalline ones, possessing liquid-like to solid-like dynamics. As examples of fluid-like systems, we use lipid bilayers and surfactant micelles ( 60 ), while for solid-like dynamics, we focus on metal surfaces ( 5 ) and nanoparticles ( 16 ). Furthermore, we also include systems with intrinsically nonuniform internal dynamics, such as, for example, a system where ice and liquid water coexist in dynamic equilibrium in correspondence with the solid–liquid transition, and soft self-assembled fibers whose behavior is dominated by local dynamic defects (see SI Appendix , Table S1 for system details) ( 6 , 7 , 50 ).…”

“…The high-dimensional data obtained using such descriptors are typically converted into lower-dimensional human-readable information via supervised and unsupervised machine learning (ML) approaches (e.g., clustering) and analyzed to characterize the internal dynamics of the studied systems ( 51 – 57 ). For example, unsupervised clustering of SOAP ( 43 ) data extracted from MD trajectories recently allowed studying the complex dynamics in self-assembling fibers, micelles, and lipid bilayers ( 47 , 50 , 58 – 60 ), in confined ionic environments ( 47 , 59 ), as well as in metal nanoparticles and surfaces ( 5 , 16 ).…”

Machine learning of microscopic structure-dynamics relationships in complex molecular systems

“…This also holds true at the molecular scale, where phenomena such as nucleation, defect propagation, and phase transitions are intricately linked to these fluctuations. The integration of advanced molecular descriptors with machine learning (ML) has been playing a key role in analyzing molecular trajectories, contributing to a better understanding of diverse nanoscale systems, ranging from atomistic to supramolecular levels [1][2][3][4][5][6][7][8][9][10][11]. Standard human-based descriptors, tailored for building detailed analyses and investigating specific systems like, i.e.…”

“…Standard human-based descriptors, tailored for building detailed analyses and investigating specific systems like, i.e. ice-water interfaces [12] or metal clusters [13,14], have increasingly left more and more space to abstract descriptors, [15][16][17][18][19][20][21] often combined with supervised and unsupervised ML methods [1][2][3][4][5][6][7][8][9][10]. These ML-based techniques offer valuable insights into the structural and dynamical properties of the systems.…”

“…These ML-based techniques offer valuable insights into the structural and dynamical properties of the systems. [5][6][7][8][9][10] While human-based approaches provide an accurate comprehension of intrigued physical-chemical mechanisms, they heavily rely on in-depth prior knowledge of the system, limiting their transferability. On the contrary, the use of abstract descriptors allows more general representations and outlines a broader picture of the system behavior, eventually managing a large amount of high-dimensional data which are often difficult to rationalize.…”