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Whether solid, liquid, or gas, a substance's state depends on the order and arrangement of its particles (atoms, molecules, or ions). Particles in the solid pack closely together, generally in a pattern. The particles vibrate about their fixed positions but do not move or squeeze past their neighbors. In liquids, although the particles are closely spaced, they are randomly arranged. The position of the particles are not fixed—that is, they are free to move past their neighbors to...
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The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
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Phase transitions play an important theoretical and practical role in the study of heat flow. In melting or fusion, a solid turns into a liquid; the opposite process is freezing. In evaporation, a liquid turns into a gas; the opposite process is condensation.
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Consider the two thermodynamic processes involving an ideal gas that are represented by paths AC and ABC in Figure 1:
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A phase diagram combines plots of pressure versus temperature for the liquid-gas, solid-liquid, and solid-gas phase-transition equilibria of a substance. These diagrams indicate the physical states that exist under specific conditions of pressure and temperature and also provide the pressure dependence of the phase-transition temperatures (melting points, sublimation points, boiling points). Regions or areas labeled solid, liquid, and gas represent single phases, while lines or curves represent...
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The phase of a given substance depends on the pressure and temperature. Thus, plots of pressure versus temperature showing the phase in each region provide considerable insights into the thermal properties of substances. Such plots are known as phase diagrams. For instance, in the phase diagram for water (Figure 1), the solid curve boundaries between the phases indicate phase transitions (i.e., temperatures and pressures at which the phases coexist).
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Neural-Network-Based Path Collective Variables for Enhanced Sampling of Phase Transformations.

Jutta Rogal1,2, Elia Schneider2, Mark E Tuckerman2,3,4

  • 1Interdisciplinary Centre for Advanced Materials Simulation, Ruhr-Universität Bochum, 44780 Bochum, Germany.

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Investigating microscopic solid-state phase transformations is difficult. We developed a new path collective variable using neural networks for efficient simulation of these complex structural changes and their kinetics.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Condensed Matter Physics

Background:

  • Investigating microscopic processes in structural phase transformations of solids presents significant challenges for both experimental and simulation approaches.
  • Atomistic simulations of solid-solid phase transitions necessitate extensive sampling of high-dimensional and often rugged energy landscapes.

Purpose of the Study:

  • To develop a more efficient method for exploring the mechanisms of solid-state phase transformations.
  • To introduce a novel path collective variable for enhanced sampling techniques in atomistic simulations.

Main Methods:

  • Construction of a one-dimensional (1D) path collective variable.
  • Definition of the path collective variable in a space spanned by global classifiers derived from local structural units.
  • Utilization of a neural-network-based classification scheme for reliable identification of local structural environments.

Main Results:

  • The proposed path collective variable facilitates efficient exploration of transformation mechanisms.
  • The method enables reliable identification of local structural environments crucial for understanding phase transitions.
  • The approach is generally applicable to various solid-state systems.

Conclusions:

  • The developed path collective variable, combined with enhanced sampling, significantly improves the efficiency of simulating solid-state phase transformations.
  • This method provides a robust framework for investigating both the mechanisms and kinetics of structural phase transitions at the atomic level.