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Molecular dynamics simulations have been extensively used to predict thermal properties, but simulating different phases with similar precision using a unified force field is often difficult, due to the lack of accurate and transferrable…
Melting properties are critical for designing novel materials, especially for discovering high-performance, high-melting refractory materials. Experimental measurements of these properties are extremely challenging due to their high melting…
Methodologies for training machine learning potentials (MLPs) to quantum-mechanical simulation data have recently seen tremendous progress. Experimental data has a very different character than simulated data, and most MLP training…
As with many parts of the natural sciences, machine learning interatomic potentials (MLIPs) are revolutionizing the modeling of molecular crystals. However, challenges remain for the accurate and efficient calculation of sublimation…
Machine learning interatomic potentials (MLIPs) are routinely used atomic simulations, but generating databases of atomic configurations used in fitting these models is a laborious process, requiring significant computational and human…
Machine-learning interatomic potentials are widely used as computationally efficient surrogates for density functional theory in atomistic simulations, enabling large-scale, long-time modeling of materials systems. We investigate how…
Precision measurements in molecules have advanced rapidly in recent years through developments in techniques to cool, trap, and control. The complexity of molecules makes them a challenge to study, but also offers opportunities for enhanced…
The usefulness of semi-analytical thermal models for predicting the connection between process, microstructure and properties in powder bed fusion has been well illustrated in recent years. Such an approach provides the promise of accuracy…
A theoretical model is developed for the interaction of intense femtosecond laser pulses with solid targets on the basis of the two-temperature equation of state for an irradiated substance. It allows the description of the dynamics of the…
The density functional theory for superconductors developed in the preceding article [cond-mat/0408685] is applied to the calculation of superconducting properties of several elemental metals. In particular, we present results for the…
Machine-learned interatomic potentials (MLIPs) are revolutionizing computational materials science and chemistry by offering an efficient alternative to {\em ab initio} molecular dynamics (MD) simulations. However, fitting high-quality…
We generalize a previously established ab initio approach-originally developed for hexagonal close-packed (hcp) metals-to accommodate solids with both internal and external degrees of freedom. This extension enables the thermodynamic and…
We have conducted a thorough theoretical and numerical investigation of the electronic susceptibility, polarizability, plasmons, their damping rates, as well as the static screening in pseudospin-1 Dirac cone materials with a flat band, or…
A computationally efficient and accurate machine-learned (ML) interatomic potential is developed for Ti$_{n+1}$C$_n$ MXenes. With a diverse set of structures computed with density functional theory, the trained ML potential demonstrates…
The interquark potential in charmonium states is calculated for the first time in both the zero and non-zero temperature phases from a first-principles lattice QCD calculation. Simulations with two dynamical quark flavours were used with…
A new approach to simulating warm and hot dense matter that combines density functional theory based calculations of the electronic structure to classical molecular dynamics simulations with pair interaction potentials is presented. The new…
For over a decade, cold atoms in lattice potentials have been an attractive platform to simulate phenomena known from solid state theory, as the Mott-insulator transition. In contrast, the field of photonics usually deals with…
An intense femtosecond-laser excitation of a solid induces highly nonthermal conditions. In materials like silicon, laser-induced bond-softening leads to a highly incoherent ionic motion and eventually nonthermal melting. But is this…
The polynomial machine learning potentials (MLPs) described by polynomial rotational invariants have been systematically developed for various systems and used in diverse applications in crystalline states. In this study, we systematically…
This work presents an investigation of the thermophysical properties of paramagnetic uranium mononitride (UN) using ab initio molecular dynamics (AIMD) simulations combined with the disordered local moment (DLM) approach. This methodology…