It is revealed that the presence of undercoordinated metal atoms close to the semiconductor-oxide interface significantly affects the magnitude of the electronic current and its propagation through MoS2.
Abstract
We introduce a new ab initio approach to simulate semiconductor devices that integrates scalable machine-learned (ML) electronic structure models with an advanced quantum transport (QT) solver. The developed framework enables 10,000X speedups over density functional theory to produce the Hamiltonian matrix of devices made of>20,000 atoms, while offering high prediction accuracy. We use its unique features to investigate MoS2/oxide samples and single-layer MoS2 field-effect transistors, where the surrounding oxide layers, here, HfO2 or Al2O3, are explicitly included into the QT domain. In particular, we reveal that the presence of undercoordinated metal atoms (Hf or Al) close to the semiconductor-oxide interface significantly affects the magnitude of the electronic current and its propagation through MoS2.
Accurate modelling of defects in materials requires quantum-level fidelity, computational efficiency, and validation against application-relevant physical observables. Density functional theory (DFT) is reliable but prohibitively expensive for large defective systems. To address this, we use a machine-learned inter...
Saurabh Kharwar, A. Vyatskikh, L. Siddiqui et al.· Communications Materials· 0 citations
Predicting charge transport in strongly anharmonic materials, particularly ultralow thermal conductors, remains a major challenge for first-principles methods. In such systems, perturbative treatments of electron-phonon interactions and the harmonic phonon picture often break down, necessitating non-perturbative approa...
Juan Zhang, Boheng Zhao, Yang Li et al.· 0 citations
While proximity-induced magnetism in van der Waals (vdW) heterostructures is often treated as uniform or analytically predictable, we reveal significant spatial fluctuations that challenge conventional modeling. Through a case study of graphene on Cr2Ge2Te6 (CGT), we reveal a profound decoupling between pseudospin-gove...
L. Cvitkovich, K. Zollner, Jaroslav Fabian· npj Computational Materials· 0 citations
(English) Understanding how atomic-scale mechanisms govern ionic and electronic transport is crucial for the design of next-generation energy materials. Here, we combine first-principles simulations and machine-learning techniques to provide predictive and transferable approaches for modelling and understanding at the...
High-throughput calculations using density-functional theory (DFT) are commonly used to explore point defects for applications in power electronics and quantum technologies. There is currently a major shift away from these traditional simulation techniques towards machine learning (ML) methods. We explore a class of ph...
P. Karlsson, J. Davidsson, R. Armiento· 0 citations
Predicting optoelectronic response across thermodynamic state space requires coupling finite-temperature nuclear dynamics to electronic structure at scales where direct first-principles calculations are impractical. Machine-learning force fields and Hamiltonian-learning models provide scalable predictions, but integrat...
Frederik Vonhoff, Jesper R. Pedersen, Frederico P. Delgado et al.· 0 citations
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