Related papers: Hybrid neural network potential for multilayer gra…
Novel materials are in great demand for future applications. The discovery of graphene, a one atom thick carbon layer, holds the promise for unique device architectures and functionalities exploiting unprecedented physical phenomena. The…
Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learning methods, hypergraph learning is a technique for exploring…
The scientific community has witnessed an exponential increase in the applications of graphene and graphene-based materials in a wide range of fields. For what concerns neuroscience, the interest raised by these materials is two-fold. On…
This paper is devoted to development of perturbation theory for studying the properties of graphene sheet of finite size, at nonzero temperature and chemical potential. The perturbation theory is based on the tight-binding Hamiltonian and…
We introduce effective field theories for the electronic properties of graphene in terms of relativistic fermions propagating in 2+1 dimensions, and outline how strong inter-electron interactions may be modelled by numerical simulation of a…
We present a first principles-quality potential energy surface (PES) describing the inter-atomic forces for hydrogen atoms interacting with free-standing graphene. The PES is a high-dimensional neural network potential that has been…
This work investigates the electronic properties of the energy spectrum of a hybrid system composed of (i) a circular quantum dot of monolayer graphene surrounded by an infinite sheet of AA-stacked bilayer graphene and (ii) a circular…
Being used in optoelectronic devices as ultra-thin conductor-insulator junctions, detailed investigations are needed about how exactly h-BN and graphene hybridize. Here, we present a comprehensive ab initio study of hot carrier dynamics…
Understanding and predicting interface diffusion phenomena in materials is crucial for various industrial applications, including semiconductor manufacturing, battery technology, and catalysis. In this study, we propose a novel approach…
Quantum confinement endows two-dimensional (2D) layered materials with exceptional physics and novel properties compared to their bulk counterparts. Although certain two- and few-layer configurations of graphene have been realized and…
Multilayered graphitic materials are not suitable as load-bearers due to their inherent weak interlayer bonding (for example, graphite is a solid lubricant in certain applications). This situation is largely improved when two-dimensional…
We explore different strategies to integrate prior domain knowledge into the design of a deep neural network (DNN). We focus on graph neural networks (GNN), with a use case of estimating the potential energy of chemical systems (molecules…
The accurate description of electrostatic interactions remains a challenging problem for fitted potential-energy functions. The commonly used fixed partial-charge approximation fails to reproduce the electrostatic potential at short range…
The work presents a study on the quantum theory of periodic graphs applied to mono- and bilayer hexagonal materials. Different parameters associated with the atoms present at the vertices of these materials were analyzed, verifying the…
We present a Graph Neural Network (GNN) that accurately simulates a multidisperse suspension of interacting spherical particles. Our machine learning framework is built upon the recent work of Sanchez-Gonzalez et al. ICML, PMLR, 119,…
Machine learning interatomic potentials (MLIPs) can predict energy, force, and stress of materials and enable a wide range of downstream discovery tasks. A key design choice in MLIPs involves the trade-off between invariant and equivariant…
Many positive electrode materials in lithium ion batteries include transition metals which are difficult to describe by electronic structure methods like density functional theory (DFT) due to the presence of multiple oxidation states. A…
Graphene stands as a promising material with vast potential across energy storage, electronics, etc. Here, we present a novel mechanical approach utilizing ultrasonic high-energy intercalation exfoliation to extract monolayer graphene from…
One of the ultimate goals of computational modeling in condensed matter is to be able to accurately compute materials properties with minimal empirical information. First-principles approaches such as the density functional theory (DFT)…
Establishing the structure-property relationship for grain boundaries (GBs) is critical for developing next generation functional materials, but has been severely hampered due to its extremely large configurational space. Atomistic…