相关论文: Insight into the effect of force error on the ther…

We assess the accuracy of six universal machine-learned interatomic potentials (MLIPs) for predicting the temperature and pressure response of materials by molecular dynamics simulations. Accuracy is evaluated across 13 diverse materials…

With the popularity of electric vehicles, the demand for lithium-ion batteries is increasing. Temperature significantly influences the performance and safety of batteries. Battery thermal management systems can effectively control the…

The thermal conductivity of nanometric objects or nanostructured materials can be determined using non equilibrium molecular dynamics (NEMD) simulations. The technique is simple in its principle, and resembles a numerical guarded hot plate…

Atomistic simulations of heat transport in complex materials are costly and hard to converge. This has led to the development of several noise-reduction techniques applicable to equilibrium molecular-dynamics (MD) simulations. We analyze…

Extracting reliable information on certain physical properties of materials, like thermal behavior, such as thermal transport, which can be very computationally demanding. Aiming to overcome such difficulties in the particular case of…

Machine learning interatomic potentials (MLIPs) offer near-ab initio accuracy with the efficiency of classical force fields, making them attractive for modeling electrolytes. Collecting a diverse training set is essential for their accuracy…

Understanding the impact of lattice imperfections on nanoscale thermal transport is crucial for diverse applications ranging from thermal management to energy conversion. Grain boundaries (GBs) are ubiquitous defects in polycrystalline…

Machine-learned interatomic potentials (MLIPs) have become the gold standard for atomistic simulations, yet their extension to magnetic materials remains challenging because spin fluctuations must be captured either explicitly or…

Machine learning interatomic potentials (MLPs) are a promising technique for atomic modeling. While high accuracy and small errors are widely reported for MLPs, an open concern is whether MLPs can accurately reproduce atomistic dynamics and…

Recently, three-component new fermions in topological semimetal MoP are experimentally observed, which may have potential applications like topological qubits, low-power electronics and spintronics. These are closely related to thermal…

We use a magnetothermal resistance method to measure lattice thermal conductivity of pure single crystal metals over a wide range of temperatures. Large transverse magnetic fields are applied to suppress electronic thermal conduction. The…

Disordered forms of carbon are an important class of materials for applications such as thermal management. However, a comprehensive theoretical understanding of the structural dependence of thermal transport and the underlying microscopic…

Machine learning potentials (MLPs) for atomistic simulations have an enormous prospective impact on materials modeling, offering orders of magnitude speedup over density functional theory (DFT) calculations without appreciably sacrificing…

The lattice thermal conductivity of the candidate thermoelectric material Mg$_3$Sb$_2$ is studied from first principles, with the inclusion of anharmonic, isotope, and boundary scattering processes, and via an accurate solution of the…

Machine learning interatomic potentials (MLIPs) have become increasingly effective at approximating quantum mechanical calculations at a fraction of the computational cost. However, lower errors on held out test sets do not always translate…

Borophene, an atomically thin, corrugated, crystalline two-dimensional boron sheet, has been recently synthesized. We investigate mechanical properties and lattice thermal conductivity of borophene via reactive molecular dynamics…

Medium and high entropy ceramics, with their distinctive disordered structures, exhibit ultra-low thermal conductivity and high temperature stability. These properties make them strong contenders for next generation thermal barrier coating…

Molecular simulations of polymer rely on accurate force fields to describe the inter-atomic interactions. In this work, we use first-principles density functional theory (DFT) calculations to parameterize a united-atom force field for…

Ensuring solid-state lithium batteries perform well across a wide temperature range is crucial for their practical use. Molecular dynamics (MD) simulations can provide valuable insights into the temperature dependence of the battery…

This work examines challenges associated with the accuracy of machine-learned force fields (MLFFs) for bulk solid and liquid phases of d-block elements. In exhaustive detail, we contrast the performance of force, energy, and stress…