Related papers: Gaussian approximation potential modeling of lithi…

A deep learning model is employed to address the challenging problem of V2O5 nanoparticle segmentation and the correlation between the chemical composition and the geometrical features of lithiated V2O5 nanoparticles as an exemplar of a…

Atomistic modeling is a widely employed theoretical method of computational materials science. It has found particular utility in the study of magnetic materials. Initially, magnetic empirical interatomic potentials or spin-polarized…

First-principles calculations within the Local Density Approximation (LDA) or Generalized Gradient Approximation (GGA), though very successful, are known to underestimate redox potentials, such as those at which lithium intercalates in…

The study of the electronic properties of charged defects is crucial for our understanding of various electrical properties of materials. However, the high computational cost of density functional theory (DFT) hinders the research on large…

Artificial neural networks (NNs) are one of the most frequently used machine learning approaches to construct interatomic potentials and enable efficient large-scale atomistic simulations with almost ab initio accuracy. However, the…

A linear regression-based machine learned interatomic potential (MLIP) was developed for the silicon-carbon system. The MLIP was predominantly trained on structures discovered through a genetic algorithm, encompassing the entire…

The market quest for fast-charging, safe, long-lasting and performant batteries drives the exploration of new energy storage materials, but also promotes fundamental investigations of materials already widely used. Presently, revamped…

For machine learning of interatomic potentials a scalable sparse Gaussian process regression formalism is introduced with a data-efficient on-the-fly adaptive sampling algorithm. With this approach, the computational cost is effectively…

As part of a project to obtain better optical response functions for nano materials and other systems with strong excitonic effects we here calculate the exchange-correlation (XC) potential of density-functional theory (DFT) at a level of…

The systematic underestimation of band gaps is one of the most fundamental challenges in semilocal density functional theory (DFT). In addition to hindering the application of DFT to predicting electronic properties, the band gap problem is…

We explore different ways to simplify the evaluation of the smooth overlap of atomic positions (SOAP) many-body atomic descriptor [Bart\'{o}k et al., Phys. Rev. B 87, 184115 (2013)]. Our aim is to improve the computational efficiency of…

Monolayer and multilayer graphene are promising materials for applications such as electronic devices, sensors, energy generation and storage, and medicine. In order to perform large-scale atomistic simulations of the mechanical and thermal…

This thesis introduces a framework that is able to describe general many-body coarse-grained interactions. We make use of this to describe the free energy surface as a cluster expansion in terms of monomer, dimer, and trimer terms. The…

Machine-learned interatomic potentials based on local environment descriptors represent a transformative leap over traditional potentials based on rigid functional forms in terms of prediction accuracy. However, a challenge in their…

Properties of many layered materials, including copper- and iron-based superconductors, topological insulators, graphite and epitaxial graphene can be manipulated by inclusion of different atomic and molecular species between the layers via…

Interacting defect systems are ubiquitous in materials under realistic scenarios, yet gaining an atomic-level understanding of these systems from a computational perspective is challenging - it often demands substantial resources due to the…

Density functional theory (DFT) calculations determine the relaxed atomic positions and lattice parameters that minimize the formation energy of a structure. We present an equivariant graph neural network (EGNN) model to predict the outcome…

Lithium-graphite intercalation compounds (Li-GICs) are the most common anode material for modern Li-ion batteries. However, the dielectric response of this material in the electrostatic limit (and its variation with the state of charge…

Machine learning interatomic potentials (MLIPs) have become widely used tools in atomistic simulations. For much of the history of this field, the most commonly employed architectures were based on short-ranged atomic energy contributions,…

Graphite, the standard commercial anode for Li-ion batteries, is thermodynamically incompatible with Na-ion batteries, leading researchers to search for alternative C-based structures (e.g., hard carbon, expanded graphite). In a simplified…