Related papers: Machine Learned Interatomic Potential for Dispersi…
Machine-learned interatomic potentials are revolutionising atomistic materials simulations by providing accurate and scalable predictions within the scope covered by the training data. However, generation of an accurate and robust training…
Using molecular dynamics simulations and integral equations (Rogers-Young, Percus-Yevick and hypernetted chain closures) we investigate the thermodynamic of particles interacting with continuous core-softened intermolecular potential.…
Thermoelectric energy conversion is an attractive technology for generating electricity from waste heat and using electricity for solid-state cooling. However, conventional manufacturing processes for thermoelectric devices are costly and…
The $\text{Cu}_7\text{P}\text{S}_6$ compound has garnered significant attention due to its potential in thermoelectric applications. In this study, we introduce a neuroevolution potential (NEP), trained on a dataset generated from ab initio…
High-Temperature Superconductors (HTS) such as YBa2Cu3O7-delta (YBCO) are essential for next-generation Tokamak fusion reactors, where Rare-Earth Barium Copper Oxides (REBCO) form the functional layers in HTS magnets. Because YBCO's…
The combinatorial approach applied to functional oxides has enabled the production of material libraries that formally contain infinite compositions. A complete ternary diagram can be obtained by pulsed laser deposition (PLD) on 100 mm…
We present a tight-binding potential based on the moment expansion of the density of states, which includes up to the fifth moment. The potential is fitted to bcc and hcp Zr and it is applied to the computation of vibrational properties of…
Germanium-Tin (GeSn) alloys have emerged as a promising material for future optoelectronics, energy harvesting and nanoelectronics owing to their direct bandgap and compatibility with existing Si-based electronics. Yet, their metastability…
We introduce a machine-learning interatomic potential for tungsten using the Gaussian Approximation Potential framework. We specifically focus on properties relevant for simulations of radiation-induced collision cascades and the damage…
New generation high-energy physics experiments demand high precision tracking and accurate measurements of a large number of particles produced in the collisions of lementary particles and heavy-ions. Silicon-tungsten (Si-W) calorimeters…
We investigate the temperature dependence of the pressure-induced softening in the negative thermal expansion material Zn(CN)$_2$ using neutron powder diffraction and molecular dynamics simulations. Both the simulation and experiment show…
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…
High-temperature, high-dose, neutron irradiation of W results in the formation of Re-rich clusters at concentrations one order of magnitude lower than the thermodynamic solubility limit. These clusters may eventually transform into brittle…
Piezoelectric crystals are widely used for acoustic wave resonators of different functioning modes and types including BAW and SAW. It is well-known that only some special orientations of crystals will exhibit desirable properties such as…
ITER-like tungsten Langmuir probes (W DLPs) have been installed and tested at the lower divertor horizontal target composed of flat-type components in EAST. Due to the non-active cooling, transient thermal and stress\strain analyses…
We developed and validated an accurate inter-atomic potential for molecular dynamics simulation in cubic silicon carbide (3C-SiC) using a deep learning framework combined with smooth Ziegler-Biersack-Littmark (ZBL) screened nuclear…
Understanding and accurately predicting hydrogen diffusion in materials is challenging due to the complex interactions between hydrogen defects and the crystal lattice. These interactions span large length and time scales, making them…
Dielectric materials, with high tunability at microwave frequencies, are key components in the design of microwave communication systems. Dense Ba0.6Sr0.4TiO3 (BST) ceramics, with different grain sizes, were prepared in order to optimise…
Simulating collision cascades and radiation damage poses a long-standing challenge for existing interatomic potentials, both in terms of accuracy and efficiency. Machine-learning based interatomic potentials have shown sufficiently high…
An optimized interatomic potential has been constructed for silicon using a modified Tersoff model. The potential reproduces a wide range of properties of Si and improves over existing potentials with respect to point defect structures and…