This research is published in R. Jana and M.A. Caro, “Searching for iron nanoparticles with a general-purpose Gaussian approximation potential”, Phys. Rev. B 107, 245421 (2023) [also available from the arXiv]. Reprinted figures are copyright (c) 2023 of the American Physical Society. The video is copyright (c) 2023 of M.A. Caro. In the field of …
Understanding the structure of amorphous carbon and silicon with machine learning atomistic modeling
When we think about semiconductors the first ones to come to mind are silicon, germanium and the III-Vs (GaAs, InP, AlN, GaN, etc.), in their crystalline forms. In fact, the degree of crystallinity in these materials often dictates the quality of the devices that can be made with them. As an example, dislocation densities as …
The details of this work are now published (open access ) in Chem. Mater. and our automated prediction tool is available from nanocarbon.fi/xps. Many popular experimental methods for determining the structure of materials rely on the periodic repetition of atomic arrangements present in crystals. A common example is X-ray diffraction. For amorphous materials, the lack …
Nanoporous carbons are an emerging class of materials with important applications in energy storage. In particular, the ability of graphitic carbons to intercalate ions is exploited in commercial Li-ion batteries where the anode is (typically) made of graphite and Li ions become electrostatically bound to the carbon host as the battery is charged: electrons are …
This post is about the following paper (and the figures are reproduced from it and are copyright of the American Physical Society): “Machine learning force fields based on local parametrization of dispersion interactions: Application to the phase diagram of C60“, by Heikki Muhli, Xi Chen, Albert P. Bartók, Patricia Hernández-León, Gábor Csányi, Tapio Ala-Nissila, and …
We are very excited to welcome our new dedicated state-of-the-art “fat node” (jargon for “high memory server”) SUMO, which we will use primarily to train machine learning GAP potentials. These potentials use lots of structural and energy/forces atomic data, and thus require large amounts of RAM. Our SUMO-I machine (we’re being optimistic that there will …
Our latest work (of a long list) on amorphous carbon simulation, titled “Machine learning driven simulated deposition of carbon films: From low-density to diamondlike amorphous carbon” just appeared in Physical Review B (https://doi.org/10.1103/PhysRevB.102.174201, check also on arXiv if you don’t have an APS subscription). This paper is the product of a lot of work, spanning …
The availability of new piezoelectric materials compatible with silicon chip integration for micro-electromechanical systems (MEMS) application is a highly attractive prospect. These new materials will help to bridge the gap between mechanical and electronic devices, making MEMS increasingly small and efficient. AlN is today’s industry’s standard and research is intensifying worldwide on AlN derivatives such as ScAlN. By alloying AlN with Sc, the crystal lattice is locally distorted due to the phase competition between the rock-salt ScN and wurtzite AlN structures, resulting in a progressive transition of AlN from wurtzite into a hexagonal-layered structure as the amount of Sc dopant atoms increases. This, in turn, induces an enhancement of the piezoelectric coefficients of ScAlN up to 50% Sc content (see figure).
Group-III nitrides are material compounds of nitrogen (N) and the elements in the first column of the p-element block of the periodic table (technically, group IIIB): boron (B), aluminium (Al), gallium (Ga), indium (In) and thallium (Tl). The compounds AlN, GaN, InN and their alloys are technologically important semiconductors with multiple applications in optoelectronics, in particular incorporated in LEDs for light production. Much of my work during my PhD at the Tyndall National Institute focused on the study of some of the properties of these materials.
Our two papers on understanding experimental X-ray spectra of materials utilizing simulated references just got accepted (and appeared as “just accepted” manuscripts) in Chemistry of Materials as Part I (more qualitative) & Part II (more quantitative). These papers are a collaboration of our group (Anja Aarva, who did most of the work, Tomi Laurila and …