Phase behavior of a two-dimensional core-softened system: new physical insights

Padilla, L.; Ramírez-Hernández, Abelardo

doi:10.1088/1361-648x/ab7e5c

Cited by 14 publications

(17 citation statements)

References 61 publications

(47 reference statements)

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“…Points and ellipses represent the mean and the covariance matrix (within one SD) of the distribution. The phase diagram in the background is constructed using data points from ( 30 ). Coexistence regions are indicated in light gray.…”

Section: Resultsmentioning

confidence: 99%

Inverse design of soft materials via a deep learning–based evolutionary strategy

et al. 2022

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Colloidal self-assembly—the spontaneous organization of colloids into ordered structures—has been considered key to produce next-generation materials. However, the present-day staggering variety of colloidal building blocks and the limitless number of thermodynamic conditions make a systematic exploration intractable. The true challenge in this field is to turn this logic around and to develop a robust, versatile algorithm to inverse design colloids that self-assemble into a target structure. Here, we introduce a generic inverse design method to efficiently reverse-engineer crystals, quasicrystals, and liquid crystals by targeting their diffraction patterns. Our algorithm relies on the synergetic use of an evolutionary strategy for parameter optimization, and a convolutional neural network as an order parameter, and provides a way forward for the inverse design of experimentally feasible colloidal interactions, specifically optimized to stabilize the desired structure.

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Section: Resultsmentioning

confidence: 99%

Inverse design of soft materials via a deep learning–based evolutionary strategy

et al. 2022

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“…Much research has been devoted to three-dimensional (3D) self-assembly [25], though a two-dimensional (2D) variant continues to be of interest from an exploratory perspective [47][48][49][50][51][52][53][54][55] as well as in applied technologies [1,56]. Optimal function is achieved via slab geometry in many devices, including optoelectronic/photonic materials [57][58][59][60][61], sensors [60,[62][63][64][65][66][67][68], display technologies [69][70][71], smart glass [72,73], spatial light modulators [74][75][76][77], and tunable filters [78][79][80][81].…”

Section: Introductionmentioning

confidence: 99%

A Bidimensional Gay-Berne Calamitic Fluid: Structure and Phase Behavior in Bulk and Strongly Confined Systems

et al. 2021

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A bidimensional (2D) thermotropic liquid crystal (LC) is investigated with Molecular Dynamics (MD) simulations. The Gay-Berne mesogen with parameterization GB(3, 5, 2, 1) is used to model a calamitic system. Spatial orientation of the LC samples is probed with the nematic order parameter: a sharp isotropic-smectic (I-Sm) transition is observed at lower pressures. At higher pressures, the I-Sm transition involves an intermediate nematic phase. Topology of the orthobaric phase diagram for the 2D case differs from the 3D case in two important respects: 1) the nematic region appears at lower temperatures and slightly lower densities, and 2) the critical point occurs at lower temperature and slightly higher density. The 2D calamitic model is used to probe the structural behavior of LC samples under strong confinement when either planar or homeotropic anchoring prevails. Samples subjected to circular, square, and triangular boundaries are gradually cooled to study how orientational order emerges. Depending on anchoring mode and confining geometry, characteristic topological defects emerge. Textures in these systems are similar to those observed in experiments and simulations of lyotropic LCs.

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“…The other 2D model we consider is the softened-core-shoulder (SCS) model [219,220], whose interaction potential is:…”

Section: Modelsmentioning

confidence: 99%

“…where is the energy scale, σ represents the typical core diameter, and k and δ are two parameters that, respectively, control the steepness and the characteristic interaction range. Similar to the HCSS, this model has been shown to stabilize a QC12 phase in a limited range of densities and temperatures and with a shoulder width of δ = 1.35σ and kσ = 10 [219,220]. Finally, we consider a three-dimensional (3D) system of rod-like particles, modelled as hardcore spherocylinders with a soft deformable corona.…”

Section: Modelsmentioning

confidence: 99%

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Artificial intelligence meets soft matter

Boattini¹

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In the following, we introduce the main aspects behind the ML techniques used throughout this thesis. Specifically, we first introduce neural networks, which are one of the most popular examples of ML algorithms for classification and regression problems, and then introduce the main ideas behind the concept of dimensionality reduction. Finally, we define the correlation where j = 1, . . . , M , and the superscript (1) indicates that the corresponding parameters are in the first layer of the network. Each quantity a j is then transformed using a differentiable, nonlinear activation function h to give z j = h(a j ).(1.4)These quantities correspond to the outputs of the units in the hidden layer. Common choices for the activation function h are sigmoidal functions such as the logistic sigmoid(1.5)where > 0 is a small positive number called learning rate. After each update, the gradient is re-evaluated for the new vector of parameters and the process is repeated until convergence. Note that the error function is defined with respect to a training set, meaning that at each step the entire training set has to be processed in order to evaluate ∇E. Techniques that use Dimensionality reductionDimensionality reduction is a typical unsupervised learning task, which is particularly useful when dealing with high-dimensional data. The goal of dimensionality reduction is to find a lower-dimensional representation of the original data while trying to preserve the most relevant information -or, equivalently, while trying to minimize the loss of information. Effectively, dimensionality reduction techniques allow us to extract the most relevant information in a given dataset by removing redundancy, which is present whenever two or more variables are strongly wherex andȳ are the means of the corresponding variables. By definition, the quantity in Eq. 1.19 is always between −1 and 1, with |r xy | = 1 indicating that the two variables are perfectly related by a linear transformation, and r xy = 0 indicating that the two variables are not at all linearly related. For instance, the two variables x 1 and x 2 in Fig. 1.2 have a Pearson correlation coefficient of r x 1 x 2 = 0.95, meaning that they are almost perfectly linearly related.Another common measure of the (possibly nonlinear) correlation between two variables is the Spearman's rank correlation coefficient, which quantifies how strongly the relationship between the two variables can be described by a monotonic function (whether linear or not). Specifically, the Spearman's rank correlation between two variables is defined as the Pearson correlation between the rank values of those two variables.

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Phase behavior of a two-dimensional core-softened system: new physical insights

Cited by 14 publications

References 61 publications

Inverse design of soft materials via a deep learning–based evolutionary strategy

Inverse design of soft materials via a deep learning–based evolutionary strategy

A Bidimensional Gay-Berne Calamitic Fluid: Structure and Phase Behavior in Bulk and Strongly Confined Systems

Artificial intelligence meets soft matter

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