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Register a new userDeep Learning Order Parameter for Polymer Phase Transition
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We report a deep learning (DL) framework viz. deep autoencoder that autonomously discovers an appropriate order parameter from molecular dynamics (MD) simulation data to characterize the coil to globule phase transition of a polymer. The deep autoencoder encodes the 3N dimensional MD trajectory of a polymer in a one-dimensional feature space and, subsequently, decodes the one-dimensional feature to its original 3N dimensional polymer trajectory. The feature space representation of a polymer provides a new order parameter that accurately describes the coil to globule phase transition as a function of temperature. This method is very generic and extensible to identify flexible order parameters to characterize wide range of phase transitions that take place in polymers and other soft materials. Moreover, this MD-DL approach is computational very efficient than a pure MD based characterization of phase transition, and has potential implications in accelerating phase prediction.
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Machine learning (ML) and artificial intelligence (AI) have the remarkable ability to classify, recognize, and characterize complex patterns and trends in large data sets. Here, we adopt a subclass of machine learning methods viz., deep learnings and develop a general-purpose AI tool - dPOLY for analyzing molecular dynamics trajectory and predicting phases and phase transitions in polymers. An unsupervised deep neural network is used within this framework to map a molecular dynamics trajectory undergoing thermophysical treatment such as cooling, heating, drying, or compression to a lower dimension. A supervised deep neural network is subsequently developed based on the lower dimensional data to characterize the phases and phase transition. As a proof of concept, we employ this framework to study coil to globule transition of a model polymer system. We conduct coarse-grained molecular dynamics simulations to collect molecular dynamics trajectories of a single polymer chain over a wide range of temperatures and use dPOLY framework to predict polymer phases. The dPOLY framework accurately predicts the critical temperatures for the coil to globule transition for a wide range of polymer sizes. This method is generic and can be extended to capture various other phase transitions and dynamical crossovers in polymers and other soft materials.
We study a phase transition in parameter learning of Hidden Markov Models (HMMs). We do this by generating sequences of observed symbols from given discrete HMMs with uniformly distributed transition probabilities and a noise level encoded in the output probabilities. By using the Baum-Welch (BW) algorithm, an Expectation-Maximization algorithm from the field of Machine Learning, we then try to estimate the parameters of each investigated realization of an HMM. We study HMMs with n=4, 8 and 16 states. By changing the amount of accessible learning data and the noise level, we observe a phase-transition-like change in the performance of the learning algorithm. For bigger HMMs and more learning data, the learning behavior improves tremendously below a certain threshold in the noise strength. For a noise level above the threshold, learning is not possible. Furthermore, we use an overlap parameter applied to the results of a maximum-a-posteriori (Viterbi) algorithm to investigate the accuracy of the hidden state estimation around the phase transition.
The insulator-metal transition in hydrogen is one of the most outstanding problems in condensed matter physics. The high-pressure metallic phase is now predicted to be liquid atomic from T=0 K to very high temperatures. We have conducted measurements of optical properties of hot dense hydrogen in the region of 1.1-1.7 Mbar and up to 2200 K. We observe a first-order phase transition accompanied by changes in transmittance and reflectance characteristic of a metal. The phase line of this transition has a negative slope in agreement with theories of the so-called plasma phase transition.
We find that conjugated polymers can undergo reversible structural phase transitions during electrochemical oxidation and ion injection. We study poly[2,5-bis(thiophenyl)-1,4-bis(2-(2-(2-methoxyethoxy)ethoxy)ethoxy)benzene] (PB2T-TEG), a conjugated polymer with glycolated side chains. Using grazing incidence wide angle X-ray scattering (GIWAXS), we show that, in contrast to previously known polymers, this polymer switches between two structurally distinct crystalline phases associated with electrochemical oxidation/reduction in an aqueous electrolyte. Importantly, we show that this unique phase change behavior has important physical consequences for ion transport. Notably, using moving front experiments visualized by both optical microscopy and super-resolution photoinduced force microscopy (PiFM), we show that a propagating ion front in PB2T-TEG exhibits non-Fickian transport, retaining a sharp step-edge profile, in stark contrast to the Fickian diffusion more commonly observed. This structural phase transition is reminiscent of those accompanying ion uptake in inorganic materials like LiFePO$_{4}$. We propose that engineering similar properties in future conjugated polymers may enable the realization of new materials with superior performance in electrochemical energy storage or neuromorphic memory applications.
A simple, empirical signature of a first order phase transition in atomic nuclei is presented, the ratio of the energy of the 6+ level of the ground state band to the energy of the first excited 0+ state. This ratio provides an effective order parameter which is not only easy to measure, but also distinguishes between first and second order phase transitions and takes on a special value in the critical region. Data in the Nd-Dy region show these characteristics. In addition, a repeating degeneracy between alternate yrast states and successive excited 0+ states is found to correspond closely to the line of a first order phase transition in the framework of the Interacting Boson Approximation (IBA) model in the large N limit, pointing to a possible underlying symmetry in the critical region.
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