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Wide bandgap semiconductors with high room temperature mobilities are promising materials for high-power electronics. Stannate films provide wide bandgaps and optical transparency, although electron-phonon scattering can limit mobilities.…
We investigate strong-coupling properties of a two-dimensional ultracold Fermi gas in the normal phase. In the three-dimensional case, it has been shown that the so-called pseudogap phenomena can be well described by a (non-self-consistent)…
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…
Modern semiconductor materials are increasingly used in multidisciplinary systems demonstrating cross-interactions between mechanical strains and electronic potentials, which gives rise to ubiquitous applications in high sensitivity,…
The appearance of generative models has opened vast chemical spaces in the design of functional materials. Although machine learning interatomic potentials (MLIPs) have substantially accelerated phonon calculations, high-fidelity prediction…
Oxide-water interfaces govern a wide range of physical and chemical processes fundamental to many fields like catalysis, geochemistry, corrosion, electrochemistry, and sensor technology. Near solid oxide surfaces, water behaves differently…
Multi-Output Gaussian Processes (MOGPs) provide a principled probabilistic framework for modelling correlated outputs but face scalability bottlenecks when applied to datasets with high-dimensional output spaces. To maintain tractability,…
The rapid development of computational materials science powered by machine learning (ML) is gradually leading to solutions to several previously intractable scientific problems. One of the most prominent is machine learning interatomic…
We introduce a framework and early results for massively scalable Gaussian processes (MSGP), significantly extending the KISS-GP approach of Wilson and Nickisch (2015). The MSGP framework enables the use of Gaussian processes (GPs) on…
Ab initio calculations were applied to large size slabs simulations for determination of the properties of the principal structures of the stoichiometric GaN(0001) surface. The results are different from published previously: stoichiometric…
Functionals of the meta-generalized gradient approximation (MGGA) are nowadays widely used in chemistry and solid-state physics for the simulation of electronic systems like molecules, solids, or surfaces. Due to their dependency on the…
Gaussian processes (GPs) are ubiquitously used in sciences and engineering as metamodels. Standard GPs, however, can only handle numerical or quantitative variables. In this paper, we introduce latent map Gaussian processes (LMGPs) that…
GaN is a wide-bandgap semiconductor used in high-efficiency LEDs and solar cells. The solid is produced industrially at high chemical purities by deposition from a vapour phase, and oxygen may be included at this stage. Oxidation represents…
Li$_{10}$Ge(PS$_6$)$_2$ (LGPS) is a highly concentrated solid electrolyte, in which Coulombic repulsion between neighboring cations is hypothesized as the underlying reason for concerted ion hopping, a mechanism common among superionic…
We present a new interatomic potential for solids and liquids called Spectral Neighbor Analysis Potential (SNAP). The SNAP potential has a very general form and uses machine-learning techniques to reproduce the energies, forces, and stress…
Beta-phase Ga2O3 has emerged as a promising candidate for a wide range of device applications, including power electronic devices, radio-frequency devices and solar-blind photodetectors. The wide bandgap energy and the predicted high…
Hydration properties of graphene oxide (GO) are essential for most of its potential applications. In this work, we employ atomistic molecular dynamics simulations to investigate seven GO compositions with different levels of oxygenation.…
We present an active learning framework for efficiently generating training data for machine-learned interatomic potentials (MLIPs). The method combines local entropy-driven molecular dynamics with global dataset-aware filtering: a…
In the graph domain, deep graph networks based on Message Passing Neural Networks (MPNNs) or Graph Transformers often cause over-smoothing of node features, limiting their expressive capacity. Many upsampling techniques involving node and…
A hallmark of meso-scale interfacial fluids is the multi-faceted, scale-dependent interfacial energy, which often manifests different characteristics across the molecular and continuum scale. The multi-scale nature imposes a challenge to…