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Updated: Aug 5, 2026
12:37
Phase Diagram Characterization Using Magnetic Beads as Liquid Carriers
Published on: September 4, 2015
A Machine-Learning-Accelerated Approach for Room-Temperature Phase Diagram Predictions
Chen Su1, Jie Lu1, Yucheng Fu2
1School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China.
The Journal of Physical Chemistry Letters
|July 27, 2026
Summary
This study introduces a machine-learning workflow for rapid, accurate finite-temperature phase diagram construction. The approach accelerates the thermodynamic screening of intermetallic systems, crucial for alloy development.
Area of Science:
- Materials Science
- Computational Materials Science
- Thermodynamics
Background:
- Phase diagrams are essential for alloy processing but their finite-temperature construction is computationally intensive.
- Accurate Gibbs free energy evaluation of candidate phases is a bottleneck in traditional methods.
Purpose of the Study:
- To develop a machine-learning workflow for efficient and accurate finite-temperature phase diagram construction.
- To accelerate the thermodynamic screening of intermetallic systems.
Main Methods:
- Coupling the crystal generator MatterGen with a fine-tuned MatterSim interatomic potential.
- Expanding candidate phase space and computing temperature-dependent phase stability.
- Validating predictions with X-ray diffraction experiments.
Main Results:
- Constructed 0 K and 300 K Gibbs phase-equilibrium diagrams for the Li-Ga-Sn ternary system.
- Predicted a temperature-induced phase assemblage switch near 230 K.
- Experimental validation of predicted room-temperature assemblages.
Conclusions:
- The machine-learning workflow provides a practical route for scalable finite-temperature phase diagram construction.
- The approach achieves accuracy comparable to density functional theory.
- Enables efficient thermodynamic screening of intermetallic systems.
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