arXiv subjects

Albert Zhu

Publications and source records attributed to Albert Zhu.

4 recordsLinked to original sources

arXiv2026

Twisted bilayer graphene from first-principles: structural and electronic properties

We present a comprehensive first-principles study of twisted bilayer graphene (tBLG) for a wide range of twist angles, with a focus on structural and electronic properties. By employing density functional theory (DFT) with an optimized local basis set, we simulate tBLG, obtaining fully relaxed commensurate structures for twist angles down to 0.987{\deg}. For all angles the lattice relaxation agrees well with continuum elastic models. For angles accessible to plane-wave DFT (VASP), we provide a detailed comparison with our local basis DFT (SIESTA) calculations, demonstrating excellent agreement in both the atomic and electronic structure. The dependence of the Fermi velocity and band width on the twist angle shows qualitative agreement with results from an `exact' $\mathbf{k \cdot p}$ continuum model, but reveals a small twist angle offset. Additionally, we provide details of the low-energy wavefunction character, band inversion and symmetries. Our results provide an ab initio reference point for the microscopic structure and electronic properties of tBLG which will serve as the foundation for future studies incorporating many-body effects.

cond-mat.mes-hall↗

arXiv2025

Wavefunction textures in twisted bilayer graphene from first principles

Motivated by recent experiments probing the wavefunctions of magic-angle twisted bilayer graphene (tBLG), we perform large-scale first-principles calculations of tBLG with full atomic relaxation across a wide range of twist angles down to $0.99^\circ$. Focusing on the magic angle, we compute wavefunctions of the low energy bands, resolving atomic-scale details and moir\'e-scale patterns that form triangular, honeycomb, and Kagome lattices. By tuning the interlayer interactions, we illustrate the formation of the flat bands from isolated monolayers and the emergence of the band inversion and fragile topology at a sufficiently large interaction strength. We identify strong indicators of a new phase transition with increasing interlayer interaction strength, achievable with external pressure or a decrease in the twist angle. When this transition occurs, the upper and lower flat bands exchange their wavefunction character and symmetry eigenvalues, which may be correlated with the appearance of superconductivity with electron doping below the magic angle. Our study demonstrates the feasibility of using first-principles wavefunctions to help interpret experimental signatures of topological and correlated phases in tBLG.

cond-mat.mes-hall↗

arXiv2025

Universal Symmetries in Twisted Moir\'e Materials

Two-dimensional multi-layer materials with an induced moir\'e pattern, either due to strain or relative twist between layers, provide a versatile platform for exploring strongly correlated and topological electronic phenomena. While these systems offer unprecedented tunability, their theoretical description remains challenging due to their complex atomic structures and large unit cells. A notable example is twisted bilayer graphene, where even the relevant symmetry group remains unsettled despite its critical role in constructing effective theories. Here, we focus on twisted bilayer graphene and use a combination of analytical methods, molecular dynamics simulations, and first-principles calculations to show that twisted atomic configurations with distinct microscopic symmetries converge to a universal interlayer structure that governs the low-energy physics. This emergent universality provides a robust foundation for symmetry-respecting models and offers insight into the role of commensurability in real twisted moir\'e systems.

cond-mat.str-el↗

arXiv2022

Fast Uncertainty Estimates in Deep Learning Interatomic Potentials

Deep learning has emerged as a promising paradigm to give access to highly accurate predictions of molecular and materials properties. A common short-coming shared by current approaches, however, is that neural networks only give point estimates of their predictions and do not come with predictive uncertainties associated with these estimates. Existing uncertainty quantification efforts have primarily leveraged the standard deviation of predictions across an ensemble of independently trained neural networks. This incurs a large computational overhead in both training and prediction that often results in order-of-magnitude more expensive predictions. Here, we propose a method to estimate the predictive uncertainty based on a single neural network without the need for an ensemble. This allows us to obtain uncertainty estimates with virtually no additional computational overhead over standard training and inference. We demonstrate that the quality of the uncertainty estimates matches those obtained from deep ensembles. We further examine the uncertainty estimates of our methods and deep ensembles across the configuration space of our test system and compare the uncertainties to the potential energy surface. Finally, we study the efficacy of the method in an active learning setting and find the results to match an ensemble-based strategy at order-of-magnitude reduced computational cost.

physics.comp-ph↗