Related papers: Machine Learning Interatomic Potential for Anisotr…

We report experimental and computational studies of thermal transport properties in hexagonal boron nitride (hBN) encapsulated molybdenum disulfide (MoS2) structure using refined optothermal Raman techniques, and reveal very high…

With the rapid development of energy storage technology, high-performance solid-state electrolytes (SSEs) have become critical for next-generation lithium-ion batteries. These materials require high ionic conductivity, excellent…

Hydrogenation of amorphous silicon (a-Si:H) is critical for reducing defect densities, passivating mid-gap states and surfaces, and improving photoconductivity in silicon-based electro-optical devices. Modelling the atomic scale structure…

Machine learning interatomic potentials (MLIPs) are inherently limited by the accuracy of the training data, usually consisting of energies and forces obtained from quantum mechanical calculations, such as density functional theory (DFT).…

The rapid development of computational materials science powered by machine learning (ML) is gradually leading to solutions to several previously intractable scientific problems. One of the most prominent is machine learning interatomic…

Machine learning interatomic potentials (MLIPs) have become powerful tools to extend molecular simulations beyond the limits of quantum methods, offering near-quantum accuracy at much lower computational cost. Yet, developing reliable MLIPs…

We show that the Gaussian Approximation Potential machine learning framework can describe complex magnetic potential energy surfaces, taking ferromagnetic iron as a paradigmatic challenging case. The training database includes total…

Two-dimensional layered materials have attracted tremendous attentions due to their extraordinary physical and chemical properties. Using first-principles calculations and Boltzmann transport theory, we give an accurate prediction of the…

High-entropy alloys (HEAs) exhibit exceptional properties arising from a combination of thermodynamic, kinetic and structural factors and have found applications in numerous fields such as aerospace, energy, chemical industries, hydrogen…

Two-dimensional materials are characterised by a number of unique physical properties which can potentially make them useful to a wide diversity of applications. In particular, the large thermal conductivity of graphene and hexagonal boron…

We study the magnetic and lattice contributions to the thermal conductivity of electrically insulating strongly spin-orbit coupled magnetically ordered phases on a two-dimensional honeycomb lattice using the Kitaev-Heisenberg model.…

Solid-state electrolytes are essential in the development of all-solid-state batteries. While density functional theory (DFT)-based nudged elastic band (NEB) and ab initio molecular dynamics (AIMD) methods provide fundamental insights on…

Artificial neural network potentials (NNPs) have emerged as effective tools for understanding atomic interactions at the atomic scale in various phenomena. Recently, we developed highly transferable NNPs for {\alpha}-iron and…

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…

Boron nitride (BN) is a material with outstanding technological promise because of its exceptional thermochemical stability, structural, electronic and thermal conductivity properties, and extreme hardness. Yet, the relative thermodynamic…

Large-scale foundation models, including neural network interatomic potentials (NIPs) in computational materials science, have demonstrated significant potential. However, despite their success in accelerating atomistic simulations, NIPs…

Universal machine learning interatomic potentials (uMLIPs) deliver near ab initio accuracy in energy and force calculations at low computational cost, making them invaluable for materials modeling. Although uMLIPs are pre-trained on vast ab…

We performed ab-initio driven density functional theory-based high throughput computations to search for materials with low thermal conductivity and high thermal transport anisotropy. We shortlisted a pool of 429 stable ternary…

Anisotropic flow of hadronic matter is considered as a sensitive tool to detect the early stage dynamics of high-energy heavy-ion collisions. Taking the event by event fluctuations of the collision geometry into account, the elliptic flow…

Machine learning interatomic potentials (MLIPs) enable efficient modeling of molecular interactions with quantum mechanical (QM) accuracy. However, constructing robust and representative training datasets that capture subtle,…