相关论文: Self-learning analytical interatomic potential des…

A machine-learned interatomic potential for Ge-rich Ge$_x$Te alloys has been developed aiming at uncovering the kinetics of phase separation and crystallization in these materials. The results are of interest for the operation of embedded…

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…

With few systems of technological interest having been studied as extensively as elemental silicon, there currently exists a wide disparity between the number of predicted low-energy silicon polymorphs and those, which have been…

In this work, we investigated tensile and compression forces effect on the thermal conductivity of silicon. We used equilibrium molecular dynamics approach for the evaluation of thermal conductivity considering different interatomic…

The development of machine learning models has led to an abundance of datasets containing quantum mechanical (QM) calculations for molecular and material systems. However, traditional training methods for machine learning models are unable…

Silicon carbide (SiC) is an attractive semiconductor material for applications in power electronic devices. However, fabrication of a high-quality SiC/SiO${}_2$ interface has been a challenge. It is well-known that there is a great…

Machine-learning potentials provide computationally efficient and accurate approximations of the Born-Oppenheimer potential energy surface. This potential determines many materials properties and simulation techniques usually require its…

We study the nature of the electronic states in the intermediate band formed by interstitial titanium in silicon. Our single-site description combines effects of electronic correlations, captured by dynamical mean-field theory, and…

Laser melting of semiconductors has been observed for almost 40 years; surprisingly, it is not well understood where most theoretical simulations show a laser-induced thermal process. $\textit{Ab initio}$ nonadiabatic simulations based on…

We perform molecular dynamics simulations of molten Lithium Fluoride using the MACE-MP-0 (small) machine learnt interatomic potential and the classical Buckingham and Born-Huggins-Mayer potentials. We find that the MACE-MP-0,…

The integration of machine learning and robotics into thin film deposition is transforming material discovery and optimization. However, challenges remain in achieving a fully autonomous cycle of deposition, characterization, and…

Understanding the mechanical properties of solid-state materials at the atomic scale is crucial for developing novel materials. For example, amorphous LiSi alloys are attractive anode materials for solid-state Li-ion batteries but face…

Materials with thermal-invariant elastic properties of materials are of interest for resonant device frequency and dimensional stability of precision devices. Here, we demonstrate that the temperature coefficient of elasticity (TCE) of…

We develop a method to fit high-temperature Gibbs free energy data for the development of interatomic potentials for atomic systems. The approach is based on Hamiltonian thermodynamic integration, enabling the identification of suitable…

Machine learning interatomic potentials (MLIPs) enable atomistic simulations with near ab initio accuracy at significantly reduced computational cost, but their broader adoption is often limited by fragmented tooling, limited scalability,…

We present an accurate interatomic potential for graphene, constructed using the Gaussian Approximation Potential (GAP) machine learning methodology. This GAP model obtains a faithful representation of a density functional theory (DFT)…

We formulate a first-principle scheme for structural optimization at finite temperature ($T$) based on the self-consistent phonon (SCP) theory, which accurately takes into account the effect of strong phonon anharmonicity. The…

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…

Effect of laser wavelength on the carrier-phonon dynamics and damage threshold of silicon is studied numerically. Laser excitation dynamics in silicon is studied using Three-Temperature Model (3TM). We consider the evolution of electron,…

The absorbed laser energy of a femtosecond laser pulse in a transparent material induces a warm dense matter region which relaxation may lead to structural modifications in the surrounding cold matter. The modeling of the…