Related papers: Fitting to magnetic forces improves the reliabilit…
We present the magnetic Moment Tensor Potentials (mMTPs), a class of machine-learning interatomic potentials, accurately reproducing both vibrational and magnetic degrees of freedom as provided, e.g., from first-principles calculations. The…
Explicit incorporation of magnetic degrees of freedom in machine-learning interatomic potentials (magnetic MLIPs) plays a crucial role in the correct description of magnetic materials and their properties. An important ingredient for…
We present a protocol for automated fitting of magnetic Moment Tensor Potential explicitly including magnetic moments in its functional form. For the fitting of this potential we use energies, forces, stresses, and magnetic forces (negative…
A recently proposed class of machine-learning interatomic potentials --- Moment tensor potentials (MTPs) --- is investigated in this work. MTPs are able to actively select configurations and parametrize the potential on-the-fly. It is shown…
Machine-learning interatomic potentials (MLIPs) have become a mainstay in computationally-guided materials science, surpassing traditional force fields due to their flexible functional form and superior accuracy in reproducing physical…
Calculations of heat transport in crystalline materials have recently become mainstream, thanks to machine-learned interatomic potentials that allow for significant computational cost reductions while maintaining the accuracy of…
A Moment Tensor Potential (MTP) has been developed for the Cu-Ag binary alloy and its accuracy, transferability, and thermodynamic fidelity evaluated. The model was trained on a diverse dataset encompassing solid, liquid, and interfacial…
We present an automated procedure for computing stacking fault energies in random alloys from large-scale simulations using moment tensor potentials (MTPs) with the accuracy of density functional theory (DFT). To that end, we develop an…
Combining the efficiency of semi-empirical potentials with the accuracy of quantum mechanical methods, machine-learning interatomic potentials (MLIPs) have significantly advanced atomistic modeling in computational materials science and…
Dual-phase $\gamma$-TiAl and $\alpha_2$-Ti$_{3}$Al alloys exhibit high strength and creep resistance at high temperatures. However, they suffer from low tensile ductility and fracture toughness at room temperature. Experimental studies show…
A machine-learned spin-lattice interatomic potential (MSLP) for magnetic iron is developed and applied to mesoscopic scale defects. It is achieved by augmenting a spin-lattice Hamiltonian with a neural network term trained to descriptors…
Medium-entropy alloys (MEAs) such as CoCrFeNi and CoCrNi are promising structural materials owing to their outstanding mechanical and thermal properties, which arise from complex chemical disorder and atomic-scale interactions. Although…
Accurate evaluation of the thermal conductivity of a material can be a challenging task from both experimental and theoretical points of view. In particular for the nanostructured materials, the experimental measurement of thermal…
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…
Ti-N material system have range of compounds with different stoichiometry like Ti2N, Ti3N2, Ti6N5, Ti4N3 alongwith Ti , TiN and solid solutions of N in Ti with a maximum of 23% solubility. In this work, we develop an interatomic potential…
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…
Machine-learning potentials for materials, namely the moment tensor potentials (MTPs), were validated using experimental EXAFS spectra for the first time. The MTPs for four metals (bcc W and Mo, fcc Cu and Ni) were obtained by the active…
We use a recently-developed machine-learned Moment Tensor Potential (MTP) trained on data generated with the density functional theory (DFT) and tailored to amorphous silicon coupled with the Activation-Relaxation Technique nouveau (ARTn)…
Machine learned potentials (MLPs) have been widely employed in molecular dynamics (MD) simulations to study thermal transport. However, literature results indicate that MLPs generally underestimate the lattice thermal conductivity (LTC) of…
Gaussian Process Regression-based Gaussian Approximation Potential has been used to develop machine-learned interatomic potentials having density-functional accuracy for free sodium clusters. The training data was generated from a large…