5 research outputs found

    DFT dilute solute diffusion in Al, Cu, Ni, Pd, Pt, Mg, Fe, W, Mo, Au, Ca, Ir, Pb, Ag, Zr

    No full text
    <p>A total of more than 400 dilute solute diffusion systems in FCC, BCC, and HCP host lattices (Al, Cu, Ni, Pd, Pt, Mg, Fe, W, Mo, Au, Ca, Ir, Pb, Ag and Zr) have been determined using density functional theory (DFT) calculations and multi-frequency diffusion models. This dataset include the jump barriers and attempt frequencies for each solute as well as final Arrhenius diffusion constant, D0, and diffusion activation barrier, Q.</p

    Thermophysical properties of FLiBe using moment tensor potentials

    No full text
    This collection contains a series of input files and data sets related to the article: Thermophysical properties of FLiBe using moment tensor potentials, Journal of Molecular Liquids, 2022</p

    CSSI Framework: Machine Learning Materials Innovation Infrastructure

    No full text
    Poster presented at the NSF CSSI 2023 PIs meeting in Houston, TX, September 26-27, 2023.</p

    Effective and interactive dissemination of diffusion data using MPContribs, plus a demo of UW/SI2 and MPContribs

    No full text
    <p>We will describe in this talk how the general approach taken by MPContribs solves the very specific challenges faced by the UW researchers in effectively disseminating their data to the public. The presented solution developed in the collaborative effort between UW and LBNL is the first to demonstrate how MPContribs can empower research groups through the rapid development and deployment of customized but MP-compatible web applications either using on-site or MP resources. It will also be shown how these efforts directly translate into solutions for the ongoing collaboration with researchers at the Advanced Light Source at LBNL [1] in which we aim to develop a processing pipeline for experimental XAS data from the beamline computer to integrated analysis web apps on MP.</p><p>In our demo portion, we show the integration of the UW/SI2 workflow with MPContribs and JupyterHub. See [2] for a quick impression of the general functionality for the UW/SI2 use case. The video and the demo illustrate how MPContribs can be used to contribute, explore and feed data to the generic contribution details pages as well as a project-specific web application.</p><p>[1] MPContribs, arXiv:1510.05024, arXiv:1510.05727, MRS Spring 2016</p><p>[2] https://www.youtube.com/watch?v=wbWde5StHnU (3:43min)</p

    Robust FCC solute diffusion predictions from ab-initio machine learning methods - Supplementary material

    No full text
    We evaluate the performance of four machine learning methods for modeling and predicting FCC solute diffusion barriers. More than 200 FCC solute diffusion barriers from previous density functional theory (DFT) calculations served as our dataset to train four machine learning methods: linear regression (LR), decision tree (DT), Gaussian kernel ridge regression (GKRR), and artificial neural network (ANN). We sep- arately optimize key physical descriptors favored by each method to model diffusion barriers. We also assess the ability of each method to extrapolate when faced with new hosts with limited known data. GKRR and ANN were found to perform the best, showing 0.15 eV cross-validation errors and predicting impurity diffusion in new hosts to within 0.2 eV when given only 5 data points from the host. We demonstrate the success of a combined DFT + data mining approach towards solving materials science challenges and predict the diffusion barrier of all available impurities across all FCC hosts.<br
    corecore