Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Phase Transitions: Sublimation and Deposition

Phase Transitions: Sublimation and Deposition

Some solids can transition directly into the gaseous state, bypassing the liquid state, via a process known as sublimation. At room temperature and standard pressure, a piece of dry ice (solid CO2) sublimes, appearing to gradually disappear without ever forming any liquid. Snow and ice sublimate at temperatures below the melting point of water, a slow process that may be accelerated by winds and the reduced atmospheric pressures at high altitudes. When solid iodine is warmed, the solid sublimes...

Phase Transitions: Vaporization and Condensation

Phase Transitions: Vaporization and Condensation

The physical form of a substance changes on changing its temperature. For example, raising the temperature of a liquid causes the liquid to vaporize (convert into vapor). The process is called vaporization—a surface phenomenon. Vaporization occurs when the thermal motion of the molecules overcome the intermolecular forces, and the molecules (at the surface) escape into the gaseous state. When a liquid vaporizes in a closed container, gas molecules cannot escape. As these gas phase molecules...

Distillation: Vapor–Liquid Equilibria

Distillation: Vapor–Liquid Equilibria

Distillation is a separation technique that takes advantage of the boiling point properties of disparate elements in a mixture. To perform distillation, we begin by heating a miscible mixture of two liquids with a significant difference in boiling points (at least 20°C). As the solution heats up and reaches the bubble point of the more volatile component, some molecules of the more volatile component transition into the gas phase and travel upward into the condenser, which is a glass tube with...

Phase Diagrams

Phase Diagrams

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...

Phase Transitions: Melting and Freezing

Phase Transitions: Melting and Freezing

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...

Phase Transitions

Phase Transitions

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 occupy...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Empowering neural network-based quantum Monte Carlo with local pseudopotentials.

Nature computational science·2026

Same author

Stochastic representation of time-evolving neural network-based wavefunctions.

The Journal of chemical physics·2025

Same author

Deep-learning electronic structure calculations.

Nature computational science·2025

Same author

Down to one network for computing crystalline materials.

Nature computational science·2025

Same author

A multi-resolution systematically improvable quantum embedding scheme for large-scale surface chemistry calculations.

Nature communications·2025

Same author

Individual and Cooperative Superexchange Enhancement in Cuprates.

Journal of chemical theory and computation·2025

Search research articles

Related Experiment Video

Updated: Jun 24, 2026

Phase Behavior of Charged Vesicles Under Symmetric and Asymmetric Solution Conditions Monitored with Fluorescence Microscopy

Phase Behavior of Charged Vesicles Under Symmetric and Asymmetric Solution Conditions Monitored with Fluorescence Microscopy

Published on: October 24, 2017

Dataset distillation for machine learning force field in phase transition regime.

Ruiyang Chen¹, Qingyuan Zhang¹, Ji Chen^1,2,3

¹School of Physics, Peking University, Beijing 100871, People's Republic of China.

The Journal of Chemical Physics

|June 23, 2026

Summary

This summary is machine-generated.

We developed a Central-Peripheral Distillation (CPD) algorithm to improve machine learning force field (MLFF) training efficiency. This method effectively distills diverse datasets, enabling accurate simulations near phase transitions with fewer configurations.

More Related Videos

Spatial Separation of Molecular Conformers and Clusters

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

Related Experiment Videos

Last Updated: Jun 24, 2026

Phase Behavior of Charged Vesicles Under Symmetric and Asymmetric Solution Conditions Monitored with Fluorescence Microscopy

Phase Behavior of Charged Vesicles Under Symmetric and Asymmetric Solution Conditions Monitored with Fluorescence Microscopy

Published on: October 24, 2017

Spatial Separation of Molecular Conformers and Clusters

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

Area of Science:

Computational Physics
Materials Science
Chemical Physics

Background:

Machine learning force fields (MLFFs) offer accurate atomistic simulations but struggle with training efficiency in phase transition regimes due to high structural fluctuations.
Current MLFF training datasets may lack sufficient diversity, particularly in complex systems undergoing phase transitions.

Purpose of the Study:

To introduce a novel dataset distillation algorithm, Central-Peripheral Distillation (CPD), to enhance MLFF training efficiency.
To improve the representation of structural diversity in training datasets for MLFFs, especially near phase transitions.

Main Methods:

Developed the Central-Peripheral Distillation (CPD) algorithm for strategic training dataset distillation.
Integrated representative samples with critical corner cases to maximize structural diversity in the distilled dataset.
Validated the CPD method on the liquid-liquid phase transition of dense hydrogen.

Main Results:

The CPD algorithm successfully distilled a diverse dataset, retaining crucial structural information.
Training an MLFF with only 150 configurations from a CPD-distilled dataset accurately reproduced the structural and dynamical properties of liquid hydrogen near its phase transition.
Demonstrated significantly improved training efficiency compared to conventional methods.

Conclusions:

The CPD algorithm enhances MLFF training efficiency and accuracy, particularly in challenging phase transition regimes.
This method enables high-fidelity dataset labeling, potentially using advanced ab initio calculations, to boost MLFF predictive power.
Paves the way for more accurate and efficient atomistic simulations of complex systems.