This is a synopsis of the paper by T. Zarrouk, R. Ibragimova, A.P. Bartók and M.A. Caro, “Experiment-Driven Atomistic Materials Modeling: A Case Study Combining X-Ray Photoelectron Spectroscopy and Machine Learning Potentials to Infer the Structure of Oxygen-Rich Amorphous Carbon”, J. Am. Chem. Soc., DOI:10.1021/jacs.4c01897, available as an Open Access PDF from the publisher’s website. …
The research highlighted in this post is part of the following paper: “Cluster-based multidimensional scaling embedding tool for data visualization”, by P. Hernández-León and M.A. Caro, Phys. Scr. 99, 066004 (2024). Available Open Access . The figures in this article are reproduced from that publication under the CC-BY 4.0 license. One of the consequences of the emergence …
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 …
It is my pleasure to announce the first GAP Developers & Users Meeting, to take place on 2nd-5th August 2022 here at Aalto University, coorganized by our group. The Gaussian approximation potential (GAP) is a theoretical/methodological framework for predicting the energy and forces in a system of interacting atoms using machine learning. GAP is also …
We have been awarded Academy of Finland funding within the framework of the EuroHPC research program, which aims to support the transition to (pre-)exascale high-performance computing (HPC) platforms and quantum computing, among others. Miguel Caro will lead the ExaFF (“Exascale-ready machine learning force fields“) consortium as Principal Investigator and Consortium Coordinator. This project is a …
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 …