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Register a new userThe uncertainty of glass transition temperature in molecular dynamics simulations and numerical algorithm for its unique determination
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Added by
Nikolay Chtchelkatchev
Publication date
2019
fields
Physics
and research's language is
English
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When the cooling rate $v$ is smaller than a certain material-dependent threshold, the glass transition temperature $T_g$ becomes to a certain degree the material parameter being nearly independent on the cooling rate. The common method to determine $T_g$ is to extrapolate viscosity $ u$ of the liquid state at temperatures not far above the freezing conditions to lower temperatures where liquid freezes and viscosity is hardly measurable. It is generally accepted that the glass transition occurs when viscosity drops by $13leq nleq17$ orders of magnitude. The accuracy of $T_g$ depends on the extrapolation quality. We propose here an algorithm for a unique determining of $T_g$. The idea is to unambiguously extrapolate $ u(T)$ to low temperatures without relying upon a specific model. It can be done using the numerical analytical continuation of $ u(T)$-function from above $T_g$ where it is measurable, to $Tgtrsim T_g$. For numerical analytical continuation, we use the Pade approximant method.
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Glass transition temperature ($T_{text{g}}$) plays an important role in controlling the mechanical and thermal properties of a polymer. Polyimides are an important category of polymers with wide applications because of their superior heat resistance and mechanical strength. The capability of predicting $T_{text{g}}$ for a polyimide $a~priori$ is therefore highly desirable in order to expedite the design and discovery of new polyimide polymers with targeted properties and applications. Here we explore three different approaches to either compute $T_{text{g}}$ for a polyimide via all-atom molecular dynamics (MD) simulations or predict $T_{text{g}}$ via a mathematical model generated by using machine-learning algorithms to analyze existing data collected from literature. Our simulations reveal that $T_{text{g}}$ can be determined from examining the diffusion coefficient of simple gas molecules in a polyimide as a function of temperature and the results are comparable to those derived from data on polymer density versus temperature and actually closer to the available experimental data. Furthermore, the predictive model of $T_{text{g}}$ derived with machine-learning algorithms can be used to estimate $T_{text{g}}$ successfully within an uncertainty of about 20 degrees, even for polyimides yet to be synthesized experimentally.
Machine learning models have emerged as a very effective strategy to sidestep time-consuming electronic-structure calculations, enabling accurate simulations of greater size, time scale and complexity. Given the interpolative nature of these models, the reliability of predictions depends on the position in phase space, and it is crucial to obtain an estimate of the error that derives from the finite number of reference structures included during the training of the model. When using a machine-learning potential to sample a finite-temperature ensemble, the uncertainty on individual configurations translates into an error on thermodynamic averages, and provides an indication for the loss of accuracy when the simulation enters a previously unexplored region. Here we discuss how uncertainty quantification can be used, together with a baseline energy model, or a more robust although less accurate interatomic potential, to obtain more resilient simulations and to support active-learning strategies. Furthermore, we introduce an on-the-fly reweighing scheme that makes it possible to estimate the uncertainty in the thermodynamic averages extracted from long trajectories. We present examples covering different types of structural and thermodynamic properties, and systems as diverse as water and liquid gallium.
A glass is a non-equilibrium thermodynamic state whose physical properties depend on time. Glass formation from the melt, as well as the inverse process of liquid structural recovery from the glass are non-equilibrium processes. A positive amount of entropy is produced during such irreversible processes. In this paper, we address the issue of the determination of entropy production during glass transition. Firstly, we theoretically determine the entropy production by means of the statistical model of a two-level system coupled to a master equation driving the time dependency of the occupancy probability of each state. Thermodynamic cycles of the type liquid-glass-liquid are considered in order to test the validity of the Clausius theorem. Secondly, we determine experimentally the production of entropy from differential scanning calorimetry experiments on the PolyVinylAcetate glass-former. Aging experiments are also considered. From the data treatments proposed here, we are able to determine the rate of production of entropy in each part of the experiments. Although being on the order of few % or less of the configurational entropy involved in the glass formation, the positive production of entropy is clearly determined. For all the thermodynamic cycles considered in these calorimetric experiments, the Clausius theorem is fulfilled.
The ultraviolet (UV) photodissociation of amorphous water ice at different ice temperatures is investigated using molecular dynamics (MD) simulations and analytical potentials. Previous MD calculations of UV photodissociation of amorphous and crystalline water ice at 10 K [S. Andersson et al., J. Chem. Phys. 124, 064715 (2006)] revealed -for both types of ice- that H atom, OH, and H2O desorption are the most important processes after photoexcitation in the uppermost layers of the ice. Water desorption takes place either by direct desorption of recombined water, or when, after dissociation, an H atom transfers part of its kinetic energy to one of the surrounding water molecules which is thereby kicked out from the ice. We present results of MD simulations of UV photodissociation of amorphous ice at 10, 20, 30, and 90 K in order to analyze the effect of ice temperature on UV photodissociation processes. Desorption and trapping probabilities are calculated for photoexcitation of H2O in the top four monolayers and the main conclusions are in agreement with the 10 K results: desorption dominates in the top layers, while trapping occurs deeper in the ice. The hydrogen atom photodesorption probability does not depend on ice temperature, but OH and H2O photodesorption probabilities tend to increase slightly (~30%) with ice temperature. We have compared the total photodesorption probability (OH+H2O) with the experimental total photodesorption yield, and in both cases the probabilities rise smoothly with ice temperature. The experimental yield is on average 3.8 times larger than our theoretical results, which can be explained by the different time scales studied and the approximations in our model.
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