High-Throughput Screening of Two-Dimensional Multifunctional Janus M ₂ X ₂ via Machine Learning Force Fields

haidi wang; Hanwen Song; Weiduo Zhu; Weiwei Chen; Zhongjun Li; Zhao Chen; Xiaofeng Liu

doi:10.1039/d6cp00867d

January 1, 2026Physical Chemistry Chemical Physics

High-Throughput Screening of Two-Dimensional Multifunctional Janus M ₂ X ₂ via Machine Learning Force Fields

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Key Points

This research aims to enhance the discovery process of two-dimensional Janus materials by utilizing machine learning technology.
Conducted high-throughput screening of Janus M2X2 materials
Employed machine learning algorithms to interpret physical properties
Explored a vast compositional space to identify promising candidates.
Successfully identified several new Janus materials with enhanced properties
Demonstrated the effectiveness of combining machine learning with high-throughput screening for material discovery
Showed potential reductions in time and resource investment for finding new materials.

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Implication

Study screens physical properties of two-dimensional Janus materials, suggesting efficient discovery methods.

Abstract

Two-dimensional (2D) Janus materials possess unique physical properties due to their broken mirror symmetry, yet their large compositional space makes systematic discovery challenging. Here, we perform a high-throughput, data-driven screening...

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Cite This Study

wang et al. (2026) studied this question.

synapsesocial.com/papers/6a0ff327d674f7c03778ba36 https://doi.org/10.1039/d6cp00867d

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High-Throughput Screening of Two-Dimensional Multifunctional Janus M ₂ X ₂ via Machine Learning Force Fields

Key Points

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Implication

Abstract

Discussion

Cite This Study

High-Throughput Screening of Two-Dimensional Multifunctional Janus M ₂ X ₂ via Machine Learning Force Fields

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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