Investigation of phase transition, mechanical behavior and lattice thermal conductivity of halogen perovskites using machine learning interatomic potentials

Shi, Yongbo; Chen, Yuanyuan; Dong, Haikuan; Wang, Hao; Qian, Ping

doi:10.1039/d3cp04657e

Phys. Chem. Chem. Phys.

2023

DOI: 10.1039/d3cp04657e

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Investigation of phase transition, mechanical behavior and lattice thermal conductivity of halogen perovskites using machine learning interatomic potentials

Yongbo Shi,

Yuanyuan Chen,

Haikuan Dong

et al.

Abstract: A clear transition among cubic (α), tetragonal (β) and orthorhombic (γ) phases was observed during the heating and cooling process.

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2024

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Cited by 2 publications

References 78 publications

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Thermal characteristics of CsPbX3 (X =Cl/Br/I) halide perovskites

Muhammed,

Mokkath

2024

Materials Today Communications

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Thermal characteristics of CsPbX3 (X =Cl/Br/I) halide perovskites

Muhammed,

Mokkath

2024

Materials Today Communications

View full text Add to dashboard Cite

Molecular dynamics simulations of heat transport using machine-learned potentials: A mini-review and tutorial on GPUMD with neuroevolution potentials

Dong,

Shi,

Ying

et al. 2024

Journal of Applied Physics

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Molecular dynamics (MD) simulations play an important role in understanding and engineering heat transport properties of complex materials. An essential requirement for reliably predicting heat transport properties is the use of accurate and efficient interatomic potentials. Recently, machine-learned potentials (MLPs) have shown great promise in providing the required accuracy for a broad range of materials. In this mini-review and tutorial, we delve into the fundamentals of heat transport, explore pertinent MD simulation methods, and survey the applications of MLPs in MD simulations of heat transport. Furthermore, we provide a step-by-step tutorial on developing MLPs for highly efficient and predictive heat transport simulations, utilizing the neuroevolution potentials as implemented in the GPUMD package. Our aim with this mini-review and tutorial is to empower researchers with valuable insights into cutting-edge methodologies that can significantly enhance the accuracy and efficiency of MD simulations for heat transport studies.

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Investigation of phase transition, mechanical behavior and lattice thermal conductivity of halogen perovskites using machine learning interatomic potentials

Abstract: A clear transition among cubic (α), tetragonal (β) and orthorhombic (γ) phases was observed during the heating and cooling process.

Cited by 2 publications

References 78 publications

Thermal characteristics of CsPbX3 (X =Cl/Br/I) halide perovskites

Thermal characteristics of CsPbX3 (X =Cl/Br/I) halide perovskites

Molecular dynamics simulations of heat transport using machine-learned potentials: A mini-review and tutorial on GPUMD with neuroevolution potentials

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