Related papers: Machine Learned Interatomic Potential for Dispersi…
One key challenge for efficiency and safety in fusion devices is the retention of tritium (T) in plasma-facing components. Tritium retention generates radioactive concerns and decreases the amount of fuel available to generate power. Hence,…
Utilizing an emergent metric developed from deep learning techniques, we determine the complex potential associated with static quarkonium. This study explores the disintegration process of quarkonium by analyzing the real component of this…
The economical lifetime of the divertor is a key concern for realizing nuclear fusion reactors that may solve the world's energy problem. A main risk is thermo-mechanical failure of the plasma-facing tungsten monoblocks, as a consequence of…
Tungsten is a promising candidate material in fusion energy facilities. Molecular dynamics (MD) simulations reveal the atomistic scale mechanisms, so they are crucial for the understanding of the macroscopic property deterioration of…
We present a combined computational and experimental investigation of the thermal properties of uranium nitride (UN), focusing on the development of a machine learning interatomic potential (MLIP) using the moment tensor potential (MTP)…
Machine-learned interatomic potentials (MLIPs) based on message passing neural networks hold promise to enable large-scale atomistic simulations of complex materials with ab initio accuracy. A number of MLIPs trained on energies and forces…
Commercial fusion power plants demand magnet materials that retain structural integrity and thermal conductivity while operating under the bombardment of energetic neutrons at cryogenic temperatures. Understanding how thermo-mechanical…
Silicon carbide (SiC) is an essential material for next generation semiconductors and components for nuclear plants. It's applications are strongly dependent on its thermal conductivity, which is highly sensitive to microstructures.…
The expansiveness of compositional phase space is too vast to fully search using current theoretical tools for many emergent problems in condensed matter physics. The reliance on a deep chemical understanding is one method to identify local…
Transition metal nitrides have been suggested to have both high hardness and good thermal stability with large potential application value, but so far stable superhard transition metal nitrides have not been synthesized. Here, with our…
The temperature driven segregation of Cr to the surface of the tungsten-based WCrY alloy is analysed with low energy ion scattering of He+ ions with an energy of 1 keV in the temperature range from room temperature to 1000 K. Due to the…
Constructing an accurate atomistic model for the high-pressure phases of tin (Sn) is challenging because properties of Sn are sensitive to pressures. We develop machine-learning-based deep potentials for Sn with pressures ranging from 0 to…
We develop a set of machine-learning interatomic potentials for elemental V, Nb, Mo, Ta, and W using the Gaussian approximation potential framework. The potentials show good accuracy and transferability for elastic, thermal, liquid, defect,…
Tungsten (W) is widely valued for its exceptional thermal stability, mechanical strength, and corrosion resistance, making it an ideal candidate for high-performance military and aerospace applications. However, its high melting point and…
We have developed a new material for neutron shielding applications where space is restricted. W$_2$B is an excellent attenuator of neutrons and gamma-rays, due to the combined gamma attenuation of W and neutron absorption of B. However,…
A relative contribution to irradiation hardening caused by dislocation loops and solute-rich precipitates is established for RPV steels of WWER-440 and WWER-1000 reactors, based on TEM measurements and mechanical testing at reactor…
New refractory alloys are being continuously designed and characterised for applications requiring good high-temperature mechanical properties and stability. Computational design from atomistic simulations is limited by interatomic…
Using the atomic cluster expansion (ACE) framework, we develop a machine learning interatomic potential for fast and accurately modelling the phonon transport properties of wurtzite aluminum nitride. The predictive power of the ACE…
Laser melting, such as that encountered during additive manufacturing (AM), produces extreme gradients of temperature in both space and time, which in turn influence microstructural development in the material. Qualification and model…
Rubber compounds for pressure sealing application typically have inferior dimensional stability with temperature fluctuations compared with their steel counterparts. This effect may result in seal leakage failures when subjected to…