6th World Congress on Integrated Computational Materials Engineering (ICME 2022): Material Databases & Platforms I
Program Organizers: William Joost; Kester Clarke, Los Alamos National Laboratory; Danielle Cote, Worcester Polytechnic Institute; Javier Llorca, IMDEA Materials Institute & Technical University of Madrid; Heather Murdoch, U.S. Army Research Laboratory; Satyam Sahay, John Deere; Michael Sangid, Purdue University

Wednesday 10:30 AM
April 27, 2022
Room: Regency Ballroom DE
Location: Hyatt Regency Lake Tahoe


10:30 AM  Invited
Now On-Demand Only- Exploring the Role of Uncertainty Quantification in Thermodynamic Data and Models: Noah Paulson1; Joshua Gabriel1; Thien Duong1; Marius Stan1; 1Argonne National Laboratory
    The thermodynamic and kinetic properties of materials are the glue that binds together discrete elements of physics-based computational workflows for materials design. In recent decades, these composition, temperature, and pressure dependent properties have been encoded in semi-empirical expressions, as exemplified by the calculation of phase diagrams (CALPHAD) approach. Traditionally, these expressions are calibrated with experimental and/or atomistic simulation results without regard to the uncertainty introduced from data and models and propagated into further models (e.g. phase field modeling) and decision-making processes. The need for quantified uncertainty has gained increasing attention in the research community and triggered the interest of computational tool developers. We present recent Argonne efforts on uncertainty quantification in thermodynamic data and models using a variety of approaches spanning atomistic data, from machine-learned force fields to Bayesian methods for parameter inference, uncertainty quantification, and automated data weighting.

11:00 AM  
FAIR Digital Object Framework and Materials Science and Engineering: Zachary Trautt1; 1National Institute of Standards and Technology
    With the increasing use of data-driven methodologies, concerns around data discovery, data access, and data interoperability have come to the forefront. Beginning in August 2019, communities have convened to work towards convergence of three complementary visions: (1) Digital Object Architecture, (2) Linked Data and Semantic Web, (3) FAIR Data Principles. This convergence has established the FAIR Digital Object Framework. This talk will overview these developments, summarize work within the NIST Material Measurement Laboratory with partners to support adoption of the FAIR Digital Object Framework within the materials science and engineering community, and provide practical examples of how researchers can leverage these developments.

11:20 AM  
Holistic Integration of Experimental and Computational Approaches and Data for Rapid Establishment of Diffusion Databases for ICME: Ji-Cheng Zhao1; Wei Zhong1; 1University of Maryland
    Substantial progresses have been made in recent years on both experimental measurements and computational predictions of diffusion coefficients. On the experimental front, diffusion multiples and liquid-solid diffusion couples together with forward-simulation analysis provide efficient and reliable determination of both impurity (dilute) diffusion and interdiffusion coefficients. Calculated diffusion coefficients based on density functional theory have also demonstrated impressive reliability, especially for the activation energy values. This talk will illustrate a novel approach to establish reliable diffusion coefficient (atomic mobility) databases by holistically integrating both experimental and computed data. This approach together with more reliable diffusion coefficient models will allow more reliable diffusion databases to be established rapidly for ICME simulations.