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Papers by Dr. Mortazavi and Prof. Zhuang ranked as "HOT" and "most popular" works

Papers by Dr. Mortazavi and Prof. Zhuang ranked as "HOT" and "most popular" works

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PhoenixD researchers’s works in the direction of machine learnng based materials exploration and design are ranked as HOT and Most Popular works in journal ‘Materials Horizons’ and ‘Nano Energy’

PhoenixD researchers work from Dr. Mortazavi and Prof. Zhuang is ranked fifth among the most cited papers of the journal Nano Energy (Impact Factor 17.6) . The paper is on "Exceptional piezoelectricity, high thermal conductivity and stiffness and promising photocatalysis in two-dimensional MoSi2N4 family confirmed by first-principles (https://doi.org/10.1016/j.nanoen.2020.105716)". In this work, by employing a combination of  density functional theory and machine learning interatomic potentials calculations, the authors explored the stability, mechanical properties, lattice thermal conductivity, piezoelectric and flexoelectric response, photocatalytic, optical and electronic features of MA2Z4 nanosheets. Their theoretical results confirm that MA2Z4 nanosheets not only undoubtedly outperform the transition metal dichalcogenides family, but also can compete with graphene for applications in nanoelectronics, optoelectronics, energy storage/conversion and thermal management systems. 

PhoenixD researchers work are among the top three HOT and Most Popular manuscripts of the Materials Horizons journal in 2023 (Impact Factor 13.3). The manuscript authored by Dr. Bohayra Mortazavi and Prof. Xiaoying Zhuang on the "Atomistic modeling of the mechanical properties: the rise of machine learning interatomic potentials (https://doi.org/10.1039/D3MH00125C )" has been ranked the second and third, respectively, among the 2023 HOT and MOST POPULAR collections of the prestigious journal of Materials Horizons. In their work, for the first time they highlight the robustness of machine learning interatomic potentials (MLIPs) in the analysis of the mechanical stability, outperforming extensively popular methods of the density functional theory and the empirical interatomic potentials. The current challenges of machine learning interatomic potential in the simulations of mechanical properties are also summarized.