Related papers: Learning glass transition temperatures via dimensi…
We introduce GlassMLP, a machine learning framework using physics-inspired structural input to predict the long-time dynamics in deeply supercooled liquids. We apply this deep neural network to atomistic models in 2D and 3D. Its performance…
A simple and predictive model is put forward explaining the experimentally observed substantial shift of the glass transition temperature, Tg, of sufficiently thin polymer films. It focuses on the limit of small molecular weight, where…
Simulation of transport properties of confined, low-dimensional fluids can be performed efficiently by means of Multi-Particle Collision (MPC) dynamics with suitable thermal-wall boundary conditions. We illustrate the effectiveness of the…
Climate models lack the necessary resolution for urban climate studies, requiring computationally intensive processes to estimate high resolution air temperatures. In contrast, Data-driven approaches offer faster and more accurate air…
Differential scanning calorimetry (DSC) is one of the most versatile probes for silicate glasses, allowing determination of, e.g., transition temperatures (glass, crystallization, melting) and the temperature dependence of heat capacity.…
Turbulent fluctuations of the atmospheric refraction index, so-called optical turbulence, can significantly distort propagating laser beams. Therefore, modeling the strength of these fluctuations ($C_n^2$) is highly relevant for the…
We give in this letter a set of general rules which allow the prediction of the value of the glass transition temperature $T_g$ in network glasses. Starting from the Gibbs-Di Marzio law which gives a very general relationship between this…
Melting is a high temperature process that requires extensive sampling of configuration space, thus making melting temperature prediction computationally very expensive and challenging. Over the past few years, I have built two methods to…
In this paper with study phase transitions of the $q$-state Potts model, through a number of unsupervised machine learning techniques, namely Principal Component Analysis (PCA), $k$-means clustering, Uniform Manifold Approximation and…
Phase-change materials (PCMs) such as Ge-Sb-Te alloys are widely used in non-volatile memory applications due to their rapid and reversible switching between amorphous and crystalline states. However, their functional properties are…
The previtreous dynamics in glass forming monomer, glycerol monoacrylate (GMA), using broadband dielectric spectroscopy (BDS) was tested. Measurements revealed the clear dynamic crossover at temperature $T_B = 254$ K and the time scale…
In metal Additive Manufacturing (AM), monitoring the temperature of the Melt Pool (MP) is crucial for ensuring part quality, process stability, defect prevention, and overall process optimization. Traditional methods, are slow to converge…
An equilibrated model glass-forming liquid is studied by mapping successive configurations produced by molecular dynamics simulation onto a time series of inherent structures (local minima in the potential energy). Using this ``inherent…
The design of efficient electrolysis devices for pure metal production requires accurate data on the properties of the melts used in the process. This work focuses on two key systems for calcium production: the molten Ca-Cu alloy and the…
Broadband dielectric spectroscopy was used to study the molecular orientational dynamics of the glass-forming, thioether-linked cyanobiphenyl liquid crystal dimers. As was expected theoretically, two different relaxation processes that…
Classification of glass-forming liquids based on the dramatic change in their properties upon approach to the glassy state is appealing, since this is the most conspicuous and often-studied aspect of the glass transition. Herein, we show…
The expansiveness of compositional phase space is too vast to fully search using current theoretical tools for many emergent problems in condensed matter physics. The reliance on a deep chemical understanding is one method to identify local…
We use a random forest model to predict the critical cooling rate (RC) for glass formation of various alloys from features of their constituent elements. The random forest model was trained on a database that integrates multiple sources of…
A data-driven framework is presented, that enables the prediction of quantities, either observations or parameters, given sufficient partial data. The framework is illustrated via a computational model of the deposition of Cu in a Chemical…
The Arrhenius crossover temperature, $T_{A}$, corresponds to a thermodynamic state wherein the atomistic dynamics of a liquid becomes heterogeneous and cooperative; and the activation barrier of diffusion dynamics becomes…