1,721,058 research outputs found
Maximum information extraction via clustering and minimization of Shannon entropy
Full text linkIn the analysis of any type of system, granting maximum information extraction from its data is non-trivial. Confidence in successful information extraction typically builds on prior knowledge of the studied system or on the user's experience. However, a robust and objective criterion for ensuring maximum information extraction from data is difficult to define. Here, we introduce a data-driven approach that employs Shannon entropy as a transferable metric to assess and quantify Maximum Information Extraction (MInE) from data via their clustering into statistically-relevant micro-domains. The method is general and can be applied virtually to any type of data or system. We demonstrate its efficiency by analyzing, as a first example, time-series data extracted from molecular dynamics simulations of water and ice coexisting at the solid/liquid transition temperature. The method allows quantifying the information contained in the data distributions (time-independent component) and the additional information gain attainable by analyzing data as time-series (i.e., accounting for the information contained in data time-correlations). The different micro-domains that can be effectively resolved and classified in the system are characterized by own entropy, which are found consistent with experimentally known thermodynamic parameters. A second test case demonstrates how the MInE approach is also effective for high-dimensional datasets and clearly shows how including little informative, but noisy, extra components/features in high-dimensional analyses may be not only useless, but even detrimental to maximum information extraction. This provides a robust parameter-free approach and quantitative metrics for data-analysis, and for the study of any type of system from its data
Research data supporting: "Self-assembly of cyclic peptide monolayers by hydrophobic supramolecular hinges"
No full text<p>This repository contains the set of modelling data shown in the paper:<strong> "Self-assembly of cyclic peptide monolayers by hydrophobic supramolecular hinges"</strong>, published on Chemical Science (DOI: 10.1039/d3sc03930g)</p>
Layer-by-layer Unsupervised Clustering of Statistically Relevant Fluctuations in Noisy Time-series Data of Complex Dynamical Systems
Full text linkComplex systems are typically characterized by intricate internal dynamics that are often hard to elucidate. Ideally, this requires methods that allow to detect and classify in unsupervised way the microscopic dynamical events occurring in the system. However, decoupling statistically relevant fluctuations from the internal noise remains most often non-trivial. Here we describe Onion Clustering : a simple, iterative unsupervised clustering method that efficiently detects and classifies statistically relevant fluctuations in noisy time-series data. We demonstrate its efficiency by analyzing simulation and experimental trajectories of various systems with complex internal dynamics, ranging from the atomic- to the microscopic-scale, in- and out-of-equilibrium. The method is based on an iterative detect-classify-archive approach. In similar way as peeling the external (evident) layer of an onion reveals the internal hidden ones, the method performs a first detection and classification of the most populated dynamical environment in the system and of its characteristic noise. The signal of such dynamical cluster is then removed from the time-series data and the remaining part, cleared-out from its noise, is analyzed again. At every iteration, the detection of hidden dynamical sub-domains is facilitated by an increasing (and adaptive) relevance-to-noise ratio. The process iterates until no new dynamical domains can be uncovered, revealing, as an output, the number of clusters that can be effectively distinguished/classified in statistically robust way as a function of the time-resolution of the analysis. Onion Clustering is general and benefits from clear-cut physical interpretability. We expect that it will help analyzing a variety of complex dynamical systems and time-series data.29 pages, 9 figures. Errors in labels in Fig5 correcte
Density-tunable pathway complexity in a minimalistic self-assembly model
Full text linkAn open challenge in self-assembly is learning how to design systems that can be conditionally guided towards different target structures depending on externally-controlled conditions. Using a theoretical and numerical approach, here we discuss a minimalistic self-assembly model that can be steered towards different types of ordered constructs at the equilibrium by solely tuning a facile selection parameter, namely the density of building blocks. Metadynamics and Langevin dynamics simulations allow us to explore the behavior of the system in and out of equilibrium conditions. We show that the density-driven tunability is encoded in the pathway complexity of the system, and specifically in the competition between two different main self-assembly routes. A comprehensive set of simulations provides insight into key factors allowing to make one self-assembling pathway prevailing on the other (or vice versa), determining the selection of the final self-assembled products. We formulate and validate a practical criterion for checking whether a specific molecular design is predisposed for such density-driven tunability of the products, thus offering a new, broader perspective to realize and harness this facile extrinsic control of conditional self-assembly
Unsupervised tracking of local and collective defects dynamics in metals under deformation
Full text linkMetals owe their unique mechanical properties to how defects emerge and propagate within their crystal structure under stress. However, the mechanisms leading from the early emerging (local) defects to the amplification of dislocations (collective plastic events) are not easy to track. Here, using tensile-stress atomistic simulations of a copper lattice as a case study, we revisit this classical problem under a new perspective based on local dynamics rather than on purely structural arguments. We use a data-driven approach that allows tracking how local fluctuations emerge and accumulate in the atomic lattice in space and time, anticipating/determining the emergence of local or collective structural defects during deformation. Building solely on the general concepts of local fluctuations and spatiotemporal fluctuation correlations, this approach allows characterizing in a unique way the evolution through the elastic, plastic, and fracture phases, describing metals as complex systems where collective phenomena originate from local dynamical triggering events
Non-trivial stimuli-responsive collective behaviours emerging from microscopic dynamic complexity in supramolecular polymer systems
Full text linkSupramolecular polymers are composed of monomers that self-assemble non-covalently generating distributions of fibres in continuous exchange-and-communication with each other and the surroundings. Intriguing collective properties may emerge in such molecular-scale complex systems, following mechanisms often difficult to ascertain. Here we show how non-trivial collective behaviours may emerge in dynamical supramolecular polymer systems already at low-complexity levels. We combine minimalistic models, simulations, and advanced statistical analyses investigating how cooperative and non-cooperative supramolecular polymer systems respond to a specific stimulus: i.e., the addition of molecular sequestrators perturbing their equilibrium. Our data show how, while in a non-cooperative system all assemblies populating the system suffer uniformly the perturbation, in a cooperative system the larger/stronger assemblies survive at the expense of the smaller/weaker entities. Collective behaviours typical of larger-scale and more complex (social, economic, etc.) systems may thus emerge even in relatively simple self-assembling systems from the internal (microscopic) dynamic heterogeneity of their ensembles
Molecular Factors Controlling the Isomerization of Azobenzenes in the Cavity of a Flexible Coordination Cage
Full text linkPhotoswitchable molecules are employed for many applications, from the development of active materials to the design of stimuli-responsive molecular systems and light-powered molecular machines. To fully exploit their potential, we must learn ways to control the mechanism and kinetics of their photoinduced isomerization. One possible strategy involves confinement of photoresponsive switches such as azobenzenes or spiropyrans within crowded molecular environments, which may allow control over their light-induced conversion. However, the molecular factors that influence and control the switching process under realistic conditions and within dynamic molecular regimes often remain difficult to ascertain. As a case study, here we have employed molecular models to probe the isomerization of azobenzene guests within a Pd(II)-based coordination cage host in water. Atomistic molecular dynamics and metadynamics simulations allow us to characterize the flexibility of the cage in the solvent, the (rare) guest encapsulation and release events, and the relative probability/kinetics of light-induced isomerization of azobenzene analogues in these host-guest systems. In this way, we can reconstruct the mechanism of azobenzene switching inside the cage cavity and explore key molecular factors that may control this event. We obtain a molecular-level insight on the effects of crowding and host-guest interactions on azobenzene isomerization. The detailed picture elucidated by this study may enable the rational design of photoswitchable systems whose reactivity can be controlled via host-guest interactions
Ephemeral ice-like local environments in classical rigid models of liquid water
Full text linkDespite great efforts over the past 50 years, the simulation of water still
presents significant challenges and open questions. At room temperature and
pressure, the collective molecular interactions and dynamics of water molecules
may form local structural arrangements that are non-trivial to classify. Here
we employ a data-driven approach built on Smooth Overlap of Atomic Position
(SOAP) that allow us to compare and classify how widely used classical models
represent liquid water. Macroscopically, the obtained results are rationalized
based on water thermodynamic observables. Microscopically, we directly observed
how transient ice-like ordered environments may dynamically/statistically form
in liquid water, even above the freezing temperature, by comparing the SOAP
spectra for different ice structures with those of the simulated liquid
systems. This confirms recent ab initio-based calculations, but also reveals
how the emergence of ephemeral local ice-like environments in liquid water at
room conditions can be captured by classical water models
Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling
Full text linkMany complex molecular systems owe their properties to local dynamic
rearrangements or fluctuations that, despite the rise of machine learning (ML)
and sophisticated structural descriptors, remain often difficult to detect.
Here we show an ML framework based on a new descriptor, named Local
Environments and Neighbors Shuffling (LENS), which allows identifying dynamic
domains and detecting local fluctuations in a variety of systems via tracking
how much the surrounding of each molecular unit changes over time in terms of
neighbor individuals. Statistical analysis of the LENS time-series data allows
to blindly detect different dynamic domains within various types of molecular
systems with, e.g., liquid-like, solid-like, or diverse dynamics, and to track
local fluctuations emerging within them in an efficient way. The approach is
found robust, versatile, and, given the abstract definition of the LENS
descriptor, capable of shedding light on the dynamic complexity of a variety of
(not necessarily molecular) systems
Machine learning of atomic dynamics and statistical surface identities in gold nanoparticles
Full text linkAbstract It is known that metal nanoparticles (NPs) may be dynamic and atoms may move within them even at fairly low temperatures. Characterizing such complex dynamics is key for understanding NPs’ properties in realistic regimes, but detailed information on, e.g., the stability, survival, and interconversion rates of the atomic environments (AEs) populating them are non-trivial to attain. In this study, we decode the intricate atomic dynamics of metal NPs by using a machine learning approach analyzing high-dimensional data obtained from molecular dynamics simulations. Using different-shape gold NPs as a representative example, an AEs’ dictionary allows us to label step-by-step the individual atoms in the NPs, identifying the native and non-native AEs and populating them along the MD simulations at various temperatures. By tracking the emergence, annihilation, lifetime, and dynamic interconversion of the AEs, our approach permits estimating a “statistical equivalent identity” for metal NPs, providing a comprehensive picture of the intrinsic atomic dynamics that shape their properties
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