Molecular Dynamics Simulations of Melting and Phase Transitions

Molecular dynamics (MD) simulations have emerged as a powerful tool to investigate the microscopic mechanisms underpinning melting and solid–liquid phase transitions in a wide range of materials. By integrating Newton’s equations of motion for atoms or molecules interacting via classical or machine-learned potentials, MD provides direct insight into nucleation, crystal–liquid coexistence and the evolution of structural motifs under varying thermodynamic conditions. Recent advancements in interatomic potentials, notably deep neural network potentials, have enabled simulations of complex high-entropy alloys and ceramics, capturing subtle effects of composition, pressure and temperature on melting temperatures and entropic behaviour. Complementary techniques, such as two-phase coexistence methods, free-energy calculations and enhanced sampling algorithms, have refined estimates of melting points within a few tens of kelvin of experimental values. Such simulations elucidate the roles of diffusion, order parameters and elastic instability in melting phenomena and guide the design of materials with tailored thermal properties for aerospace, energy and additive-manufacturing applications.

Research from Nature Portfolio

Recent studies have harnessed machine-learning potentials to model melting in elaborately structured compounds. One investigation employed a deep neural network potential to explore melting in high-entropy carbonitrides across varying nitrogen contents. By simulating heating and cooling cycles, researchers quantified the nonlinear enhancement of melting temperature associated with compositional tuning, elucidating atomistic pair correlations and entropy variations in the liquid phase. The approach achieved quantitative predictions of melting temperatures within experimentally relevant margins and demonstrated pathways to optimise thermal resilience in functional carbides.

Molecular Dynamics Simulations of Melting and Phase Transitions publication trend

The graph below shows the total number of articles in molecular dynamics simulations of melting and phase transitions across all publications each year (not limited to Nature Index journals).

Technical terms

Molecular dynamics simulation: A computational technique that integrates Newton’s equations of motion to track the time evolution of a system of interacting atoms or molecules.

Interatomic potential: A mathematical function describing the energy between particles as a function of their positions; examples include empirical, ab initio and machine-learning potentials.

Deep neural network potential: A machine-learning model trained on quantum-mechanical data to predict potential energies and forces with near first-principles accuracy at reduced cost.

Two-phase coexistence method: A simulation protocol in which solid and liquid phases are brought into contact under controlled temperature and pressure to directly determine the melting point from interface dynamics.

Order parameter: A quantitative measure of structural order in a system, used to distinguish phases, such as crystalline versus liquid arrangements.

Elastic instability criterion: A method for predicting melting based on the vanishing of appropriate elastic moduli computed at finite temperatures.

References

1. Melting simulations of high-entropy carbonitrides by deep learning potentials. Scientific Reports (2024).
2. A Molecular Dynamics Study of Cyanate Ester Monomer Melt Properties. Polymers (2022).
3. Modified Born method for modeling melting temperature using ab initio molecular dynamics. Journal of Physics Condensed Matter (2023).
4. Melting Curve of Cobalt using Molecular Dynamics Simulation. Prithvi Academic Journal (2023).