arXiv subjects

Anoj Aryal

Publications and source records attributed to Anoj Aryal.

3 recordsLinked to original sources

arXiv2026

Ideal Topological Flat Bands in Two-dimensional Moiré Heterostructures with Type-II Band Alignment

Topological flat bands play an essential role in inducing exotic interacting physics, ranging from fractional Chern insulators to superconductivity, in moiré materials. In this work, we propose a design principle for realizing topological flat bands with "ideal quantum geometry", namely the trace of Fubini-Study metric equals to the Berry curvature, in a class of two-dimensional moiré heterostructures with type-II band alignment. We first introduce a moiré Chern-band model to describe this system and show that topological flat bands can be realized in this model when the moiré superlattice potential is stronger than the type-II atomic band gap of the heterostructure. Next, we map this model into a topological heavy fermion model that consists of a localized orbital for "f-electron" and a conducting band for "c-electron". We find that both the flatness and quantum geometry of the flat band in the topological heavy fermion model depend on the energy gap between c-electron and f-electron bands at $Γ$ which is experimentally controllable via external gate voltages. This tunability will allow us to realize an ideal topological flat band with zero band-width and ideal quantum geometry. Our design strategy of topological flat bands is insensitive of twist angle. We also discuss possible material candidates for moiré heterostructures with type-II band alignment.

cond-mat.mes-hall↗

arXiv2026

Materials design based on a material-motif network and heterogeneous graphs

Machine learning models for functional materials design require precise and informative representations of material systems. Common representations encode atomic composition and bonding but often do not include local coordination environments across chemically diverse crystals. Recurring structural motifs provide a motif level description of crystalline solids and can serve as interpretable descriptors for structure property learning. To analyze the motif connectivity in materials, we construct a bipartite material motif network from 131,548 Materials Project entries, with materials and motifs as the two node sets. Edges connect materials to their constituent motifs and are weighted by motif distortion, which quantifies the strength of each material motif association. Network connectivity is analyzed to identify motif-defined material clusters that capture recurring local geometries relevant to structure property trends. Most shared motifs act as hubs that connect otherwise disconnected regions of the network, enabling motif guided screening by expanding from known motifs to nearby materials in the same neighborhoods. A network embedding step converts this weighted connectivity into vector representations of materials. Using these motif informed embeddings, property prediction yields a formation energy mean absolute error (MAE) of 0.157 eV per atom and a bandgap MAE of 0.601 eV. These results indicate that motif connectivity provides a compact, interpretable representation that complements existing descriptors for scalable screening and structure property modeling.

cond-mat.mtrl-sci↗

arXiv2022

Incorporation of density scaling constraint in density functional design via contrastive representation learning

In a data-driven paradigm, machine learning (ML) is the central component for developing accurate and universal exchange-correlation (XC) functionals in density functional theory (DFT). It is well known that XC functionals must satisfy several exact conditions and physical constraints, such as density scaling, spin scaling, and derivative discontinuity. In this work, we demonstrate that contrastive learning is a computationally efficient and flexible method to incorporate a physical constraint in ML-based density functional design. We propose a schematic approach to incorporate the uniform density scaling property of electron density for exchange energies by adopting contrastive representation learning during the pretraining task. The pretrained hidden representation is transferred to the downstream task to predict the exchange energies calculated by DFT. The electron density encoder transferred from the pretraining task based on contrastive learning predicts exchange energies that satisfy the scaling property, while the model trained without using contrastive learning gives poor predictions for the scaling-transformed electron density systems. Furthermore, the model with pretrained encoder gives a satisfactory performance with only small fractions of the whole augmented dataset labeled, comparable to the model trained from scratch using the whole dataset. The results demonstrate that incorporating exact constraints through contrastive learning can enhance the understanding of density-energy mapping using neural network (NN) models with less data labeling, which will be beneficial to generalizing the application of NN-based XC functionals in a wide range of scenarios that are not always available experimentally but theoretically justified. This work represents a viable pathway toward the machine learning design of a universal density functional via representation learning.

physics.chem-ph↗