Data-Efficient Training of Machine Learning Interatomic Potential for MAX-Phase Synthesizability Prediction

Jonghyuk Kim; Woojin Shin; Jungjae Hwang; Joon Ho Kwon; Sanghyeon Song; Kyoungmin Min

doi:10.1039/d5ta09736c

January 1, 2026Journal of Materials Chemistry A

Data-Efficient Training of Machine Learning Interatomic Potential for MAX-Phase Synthesizability Prediction

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Key Points

To develop a data-efficient machine learning model for predicting the synthesizability of high-entropy MAX phases.
Utilized machine learning techniques to refine interatomic potentials.
Employed data-efficient training methodologies to improve model accuracy.
Analyzed the relationship between MAX phase features and synthetic outcomes.
Demonstrated a high accuracy in predicting the synthesizability of HE-MAX phases.
Showed improved data efficiency in training compared to conventional methods.
Identified key features that influence the synthesis of MAX phases.

Implication

Data-efficient training predicts synthesizability of MAX phases, suggesting advancements in material design.

Abstract

High-Entropy MAX phases (HE-MAX) hold the potential to extend the desirable characteristics of the MXene, including high electrical conductivity, enhanced catalytic activity, and low ion diffusion barriers. However, the synthesis...

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Cite This Study

Kim et al. (2026) studied this question.

synapsesocial.com/papers/69a1351ded1d949a99abeb3e https://doi.org/10.1039/d5ta09736c

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Data-Efficient Training of Machine Learning Interatomic Potential for MAX-Phase Synthesizability Prediction

Key Points

Implication

Abstract

Discussion

Cite This Study

Data-Efficient Training of Machine Learning Interatomic Potential for MAX-Phase Synthesizability Prediction

Key Points

Implication

Abstract

Discussion

Cite This Study

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