8th World Congress on Integrated Computational Materials Engineering (ICME 2025): ICME for Materials and Process Design I
Program Organizers: Victoria Miller, University of Florida; Stephen DeWitt, Oak Ridge National Laboratory
Wednesday 9:00 AM
June 18, 2025
Room: Platinum Ballroom 5
Location: Anaheim Marriott
Session Chair: Stephen DeWitt, Oak Ridge National Laboratory
9:00 AM Invited
An Approach to Implementing Sustainability Into Structural Materials Design: Carelyn Campbell1; 1National Institute of Standards and Technology
The demand for more sustainable alloys and processes requires a new element in the well-defined processing-structure-property-performance systems approach. As the design process is always a balancing of various conflicting objectives, several key challenges in incorporating sustainability and recyclability into the design process are considered. What are the available tools to evaluate different processes methods and end-life recyclability of the designed material? What tools are needed to evaluate available scrap materials that may reduce cost, energy consumption, and emissions? How can the amount of scrap material be increased in the primary alloy content without impacting the desired performance? How does one identify critical materials and design for replacement alloying elements? Much of the infrastructure required to meet these new challenges have been developed through the Materials Genome Initiative. Some examples of the tools available to meet these challenges are applied to different Al, steel, and superalloy designs.
9:30 AM
Rapid and Flexible Design of Alloys Using The Alloy Optimization Software (TAOS): Nicholas Ury1; Brandon Bocklund1; Vincenzo Lordi1; Aurelien Perron1; 1Lawerence Livermore National Laboratory
The Alloy Optimization Software (TAOS) is a user-friendly tool that allows for rapid design of alloys through black-box optimization. By coupling with Calphad-based software packages such as PyCalphad and Thermo-Calc, users can leverage their own private or unencrypted databases, commercial databases, or a combination thereof to meet their specific alloy optimization needs. With a “bring-your-own-model” feature, TAOS also allows users to go beyond Calphad-based calculations and incorporate custom properties into their alloy design. In addition, TAOS provides bi-objective optimization and sensitivity analysis that enables visualizing the relationships between the composition and desired properties. Case studies will be presented to showcase TAOS’s user-friendly interface and how users are able to create their own models and utilize these new features to aid in their alloy design efforts. TAOS can be directly licensed by end users: https://softwarelicensing.llnl.gov/product/taos.Prepared by LLNL under Contract DE-AC52-07NA27344.
9:50 AM
Predicting the Color of Gold-Based Alloys Using a CALPHAD-Based Model: Incorporating Composition, Heat Treatment, and Viewing Conditions: Xin Wang1; Bartek Kaplan2; Clay Houser1; Paul Mason1; Andreas Markstrom2; 1Thermo-Calc Software Inc; 2Thermo Calc Software Ab
We present a CALPHAD-based computational model for predicting the color of gold alloys, considering critical factors such as alloy composition, heat treatment temperature, illuminant wavelength distribution, observer viewing angle, and material thickness. This model leverages thermodynamic databases to account for the presence of intermetallic compounds, enabling comprehensive simulations of gold-based alloy colors. Core methodology involves the calculation of the dielectric function for each phase present in the alloy, which is then used to predict the material's optical properties. For single-layer systems, the Fresnel equations are applied to model light reflection and transmission. For more complex structures, such as multi-layered configurations, a transfer matrix method is utilized. This multi-faceted approach allows for precise color predictions under varying conditions, providing a robust tool for designing gold alloys with tailored aesthetic properties. Our model offers significant advancements in the prediction and design of gold alloys where precise control over material appearance is essential.
10:10 AM
Exploring Alloying Strategies for Enhanced Strength and Ductility in High-Temperature Additively Manufactured Lightweight Alloys: Avik Mahata1; 1Merrimack College
Additive manufacturing (AM) of high-strength aluminum alloys faces significant challenges due to rapid melting and solidification, leading to brittleness from excessive intermetallics and reduced ductility under tensile stress at elevated temperatures. To overcome these limitations, we explore novel alloying strategies by introducing elements such as Ti, Zr, Ni, Sc, Mn, and Ce into the aluminum matrix. Utilizing density functional theory (DFT) and molecular dynamics (MD) modeling, we investigate how targeted alloying can stabilize intermetallic phases, prevent coarsening, and refine microstructures to enhance tensile ductility. By forming thermally stable, nanoscale precipitates and optimizing the balance between soft and hard phases, we aim to reduce brittleness and improve deformability without compromising strength. This research provides a computational framework for designing aluminum alloys capable of withstanding the thermal and mechanical demands of high temperature AM applications.
10:30 AM Break
10:50 AM
Computationally Guided Alloy Design for Refractory Complex Concentrated Alloys With Tensile Ductility Made by Laser Powder Bed Fusion: Dillon Jobes1; Daniel Rubio-Ejchel1; Jacob Hochhalter2; Amit Misra2; Liang Qi1; Yong-Jie Hu3; Jerard Gordon1; 1University of Michigan; 2University of Utah; 3Drexel University
Single-phase body-centered cubic refractory complex concentrated alloys (RCCAs) retain strength at high temperatures but typically lack room-temperature ductility, causing cracking and premature failure when processed by laser-based additive manufacturing (AM). We developed an alloy design framework for enhancing tensile ductility in non-equimolar RCCAs within the Ti–V–Cr–Nb–Zr–Ta system. Density functional theory (DFT) and machine learning screened ~10⁶ alloy compositions for ductility, followed by Scheil solidification modeling to exclude compositions prone to micro-segregation and cracking. Two alloys, Ti0.4Zr0.4Nb0.1Ta0.1 and Ti0.486V0.375Ta0.028Cr0.111, were selected, fabricated via laser powder bed fusion (L-PBF) AM, and evaluated for solidification defects, microstructure, and mechanical properties. Both alloys achieved tensile yield strengths >800 MPa and failure strains >5%. Premature failure due to un-melted high melting point elements was observed, highlighting areas for further optimization. These results demonstrate the framework's effectiveness in designing ductile RCCAs for AM applications.
11:10 AM
Multidisciplinary Inverse Robust Co-Design Exploration Framework for Materials, Products, and Manufacturing Processes: H M Dilshad Alam Digonta1; Maryam Ghasemzadeh2; Anton van Beek2; Anand Balu Nellippallil1; 1Florida Institute of Technology; 2University College Dublin
Realizing Integrated Computational Materials Engineering (ICME) from a design perspective involves co-considering the interactions between stakeholders from manufacturing, materials, and product disciplines. Effectively realizing the product-material-manufacturing system requires a systems-based, top-down, inverse design approach that supports formulating the multidisciplinary problem and coordinating the couplings and interactions using simulation models. This necessitates the capability to carry out inverse robust co-design, which involves simultaneously exploring a range of robust design solutions that satisfy stakeholders' preferences across multiple disciplines while managing uncertainties.In this paper, we present a multidisciplinary, inverse, robust co-design framework for design space exploration involving multiple disciplines and the management of uncertainty. The framework integrates (i) data-driven Gaussian Process (GP) models for establishing the Processing-microStructure-Property-Performance (PSPP) linkages, (ii) Bayesian inference methods together with robust design and decision support problem constructs for inverse robust co-design problem formulation, and (iii) interpretable Self-Organizing Maps (iSOM) to simultaneously visualize and explore the multidisciplinary design space. The efficacy of the framework is demonstrated using an industry-inspired problem involving the hot rod rolling process, which considers the interactions between material, microstructure, and manufacturing conditions. The framework generalizes to a broad range of manufacturing processes and materials and supports the co-design exploration of systems characterized by multidisciplinary interactions and the presence of uncertainties.
11:30 AM
Enhancing Robust Design Using Improved Variance Estimation in Design Capability Index: Pooja Mukundan1; H M Dilshad Alam Digonta1; Mathew Baby1; Anand Balu Nellippallil1; 1Florida Institute of Technology
The simulation-based design of material systems involves uncertainties in design variables that must be managed. One approach for managing design variable uncertainties involves using the Design Capability Index (DCI) metric for Robust Design, which allows designers to identify robust solutions that are relatively insensitive to uncertainties. In DCI, the variance of responses due to uncertainties is computed using the first-order Taylor Series expansion, which fails to eliminate ‘uncertainty sensitive’ maxima/minima points from the robust solution space. First-order Taylor Series Expansion will also potentially result in errors for non-linear/multimodal response functions. To address these drawbacks, we explore alternative variance estimation methods – the second derivative, multiple derivative, and multiple-point methods, in the DCI metric. We employ the hot rod rolling problem to explore the effect of these alternate methods on DCI. This exploration is facilitated using the machine learning-based visualization technique - interpretable Self-Organizing Map (iSOM).
11:50 AM
Intelligent Materials Design for Rhenium Refractory Complex Concentrated Alloys: Abdelrahman Garbie1; 1University of Louisiana Lafayette
This study focuses on the computational design of Refractory Complex Concentrated Alloys (Re-RCCAs), leveraging ICME frameworks to integrate empirical data, machine learning models, and alloy design methodologies. Our approach optimizes Re-RCCA compositions for extreme environments requiring mechanical strength, thermophysical stability, and radiation tolerance. We employ CALPHAD simulations, high-throughput semi-empirical calculations, and machine learning-based modeling to minimize waste and enhance efficiency. Machine learning predicts optimal compositions while reducing experimental iterations and material waste, following a Lean Six Sigma approach.The ICME workflow involves literature review, element selection, HEAPS screening, and CALPHAD modeling to predict phase stability. Machine learning then co-optimizes alloy compositions, significantly reducing trials. This efficient, cost-effective process maximizes resource use and minimizes loss of expensive powders, highlighting ICME's potential in advanced alloy design.
12:10 PM
Study on Hydrogen Embrittlement in Steels Using First Principles Calculations: Sanyam Totade1; Abhishek Thakur1; Appa Chintha1; 1Tata Steel
Hydrogen embrittlement leads to serious degradation in toughness and strength of steels. This poses an important aspect for industries to mitigate hydrogen embrittlement in steels to increase their usability and prevent failure especially under hydrogen environment. In this context, using first-principles calculations, we have studied the energetics of hydrogen in different structural traps, such as voids, dislocations, and grain boundaries, present in BCC Fe. Hydrogen stability under the presence of common solute atoms present in steel has also been studied. Finally, hydrogen behavior at Fe/precipitate interface has been investigated with a focus on different carbides potentially present in steel. Our findings show that tetrahedral voids present in Fe stabilizes hydrogen more as compared to the octahedral voids. Also, the presence of Si, Co, Cr, Mn, and S among other common solutes traps hydrogen efficiently within Fe.Keywords: Hydrogen Embrittlement; First-principles calculations; Structural traps; Precipitates.