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Group-VI transition metal dichalcogenides (TMDs), MoS$_2$ and MoSe$_2$, have emerged as prototypical low-dimensional systems with distinctive phononic and electronic properties, making them attractive for applications in nanoelectronics,…
Among emerging energy materials, halide and chalcogenide perovskites have garnered significant attention over the last decade owing to the abundance of their constituent species, low manufacturing costs, and their highly tunable…
The mechanical properties of hybrid perovskite materials are important for device flexibility, resistance to fracture, epitaxial growth, surface energetics of quantum dots, and induction or relief of stress in thin films due to thermal…
On the basis of the self-consistent phonon theory and the special displacement method, we develop an approach for the treatment of anharmonicity in solids. We show that this approach enables the efficient calculation of…
We assess the capability of recently developed foundational atomistic models (FAMs) to simulate iron alloys under the extreme pressures and temperatures of Earth's core. Static equations of state of hexagonal close-packed (hcp) and…
Fluids with competing short range attraction and long range repulsive interactions between the particles can exhibit a variety of microphase separated structures. We develop a lattice-gas (generalised Ising) model and analyse the phase…
Organic-inorganic halide perovskites present a number of challenges for first-principles atomistic materials modelling. These `plastic crystals' feature dynamic processes across multiple length-scales and time-scales, which include: (i)…
Accurate prediction of atomistic, thermodynamic, and kinetic properties from molecular structures underpins materials innovation. Existing computational and experimental approaches lack the scalability required to navigate chemical space…
Machine learning force fields (MLFFs) have emerged as a sophisticated tool for cost-efficient atomistic simulations approaching DFT accuracy, with recent message passing MLFFs able to cover the entire periodic table. We present an invariant…
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…
Inorganic metal halide perovskites are promising materials for next-generation technologies due to a plethora of unique physical properties, many of which cannot be observed in the oxide perovskites. On the other hand, the search for…
An accurate and easily extendable method to deal with lattice dynamics of solids is offered. It is based on first-principles molecular dynamics simulations and provides a consistent way to extract the best possible harmonic - or higher…
Lead-free halide double perovskites provide a promising solution for the long-standing issues of lead-containing halide perovskites, i.e., the toxicity of Pb and the low stability under ambient conditions and high-intensity illumination.…
The phonon dispersion relations of crystal lattices can often be well-described with the harmonic approximation. However, when the potential energy landscape exhibits more anharmonicity, for instance, in case of a weakly bonded crystal or…
Advances in machine learning have led to the development of foundation models for atomistic materials chemistry, enabling quantum-accurate descriptions of interatomic forces across chemically diverse compounds at reduced computational cost.…
The anharmonic lattice dynamics of oxide and halide perovskites play a crucial role in their mechanical and optical properties. Raman spectroscopy is one of the key methods used to study these structural dynamics. However, despite decades…
Using first-principles based Density Functional Theory (DFT), we have investigated the structural instabilities in the austenite phases of Mn$_{2}$Ni{\it X} ({\it X}= Al, Ga, In, Sn) magnetic shape memory alloys (MSMA). A complete softening…
Metal halide perovskites have emerged as an exciting class of materials for applications in solar energy harvesting, optical devices, catalysis, and other photophysical applications. Many of the exciting properties of halide perovskites are…
Alloy-based perovskite solar cells offer tunable properties and improved stability, but their complexity has impeded accurate modeling, hindering development. We present a machine-learning (ML) accelerated atomistic modeling approach for…
Three-dimensional lattices are fundamental to solid-state physics. The description of a lattice with an atomic basis constitutes the necessary information to predict solid phase properties and evolution. Here, we present a new algorithm for…