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Phase Transitions: Melting and Freezing02:39

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Heating a crystalline solid increases the average energy of its atoms, molecules, or ions, and the solid gets hotter. At some point, the added energy becomes large enough to partially overcome the forces holding the molecules or ions of the solid in their fixed positions, and the solid begins the process of transitioning to the liquid state or melting. At this point, the temperature of the solid stops rising, despite the continual input of heat, and it remains constant until all of the solid is...
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The Frost circle or the inscribed polygon method is a graphical method for determining the relative energies of π molecular orbitals (MOs) for planar, fully conjugated, and monocyclic compounds. This method was first described by A. A. Frost and Boris Musulin in 1953.
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Crystalline solids are divided into four types: molecular, ionic, metallic, and covalent network based on the type of constituent units and their interparticle interactions.
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Crystal Field Theory
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An Externally-Heated Diamond Anvil Cell for Synthesis and Single-Crystal Elasticity Determination of Ice-VII at High Pressure-Temperature Conditions
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A computationally efficient quasi-harmonic study of ice polymorphs using the FFLUX force field.

Alexandra Pák1, Matthew L Brown1, Paul L A Popelier1

  • 1Department of Chemistry, University of Manchester, Oxford Road, Manchester, M13 9PL, United Kingdom.

Acta Crystallographica. Section A, Foundations and Advances
|December 19, 2024
PubMed
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FFLUX, a machine-learned force field, efficiently predicts ice polymorph stability. It accurately models water, enabling faster simulations and thermodynamic phase diagram construction.

Keywords:
ice structuresmachine learningpolymorphismquantum chemical topologyquasi-harmonic approximation

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Area of Science:

  • Computational chemistry and materials science.
  • Development of novel machine learning models for molecular simulations.

Background:

  • Accurate prediction of condensed-phase material properties requires robust force fields.
  • Existing methods for polymorph stability assessment can be computationally expensive.
  • Machine learning offers a promising avenue for developing efficient and accurate force fields.

Purpose of the Study:

  • To introduce and validate FFLUX, a multipolar machine-learned force field for predicting polymorph stability.
  • To assess the thermodynamic stability of different ice phases (Ih, II, XV) using FFLUX.
  • To explore the potential of FFLUX for constructing temperature- and pressure-dependent phase diagrams.

Main Methods:

  • FFLUX utilizes Gaussian process regression models trained on quantum chemical topology data.
  • A water monomer model was developed with sub-kJ/mol accuracy.
  • Simulations employed a Lennard-Jones potential for intermolecular interactions.
  • Lattice dynamics and quasi-harmonic approximation were used for stability and free energy calculations.

Main Results:

  • FFLUX achieved high accuracy for water monomer energies.
  • FFLUX-optimized lattice constants were comparable to PBE+D3, with significant speedups (10^3-10^5x).
  • Ices Ih and XV were found dynamically stable; ice II stability was mispredicted due to the non-bonded potential.
  • A new, dynamically stable ice phase (II') was identified by FFLUX.
  • FFLUX enabled the first Gibbs free energy calculations for ice polymorphs via quasi-harmonic approximation.

Conclusions:

  • FFLUX provides an efficient and accurate method for predicting polymorph energies and stability.
  • The model demonstrates potential for exploring complex phase diagrams of materials.
  • Further refinement of non-bonded potentials may be necessary for accurate prediction of certain ice phases.