Session Sheet - 8th World Congress on Integrated Computational Materials Engineering (ICME 2025): ICME for Materials and Process Design II

8th World Congress on Integrated Computational Materials Engineering (ICME 2025): ICME for Materials and Process Design II
Program Organizers: Victoria Miller, University of Florida; Stephen DeWitt, Oak Ridge National Laboratory

Thursday 8:00 AM
June 19, 2025
Room: Platinum Ballroom 3
Location: Anaheim Marriott

Session Chair: Stephen DeWitt, Oak Ridge National Laboratory

8:00 AM  Invited
ICME-Based Materials and Manufacturing Development: Qiaofu Zhang¹; ¹University of Alabama
    The Integrated Computational Materials Engineering (ICME) approach has been developed and demonstrated as a cost-effective and time-efficient strategy for the development of novel materials and advanced manufacturing processes over the past decade. This talk will present examples of new materials design and advanced manufacturing process development through the integration of ICME modeling with experiments, replacing the conventional trial-and-error strategy. It includes two main aspects: 1) the development of ICME models to link the materials’ process-structure-property relationships through CALPHAD-based (CALculation of PHAse Diagram) microstructure modeling, physics-based models, and machine learning approaches; 2) iterative designs that combine ICME-based computer design with lab-scale prototyping. Specific examples will include the development of high-performance conductive alloys for energy applications, ceramic-metal composite for extreme environment applications, and innovations in advanced manufacturing.

8:30 AM
AI-Enhanced Integrated Computational Materials Engineering Framework for Efficient Alloy Design in Continuous Design Spaces: Osman Mamun¹; Bhuiyan Shameem Mahmood Ebna Hai¹; ¹Fehrmann MaterialsX GmbH, Fehrmann Tech Group
    The rapid discovery of multi-component alloys with optimized properties is challenging, requiring both computational efficiency and precision. Recent Bayesian optimization (BO) advancements in continuous design spaces enhance alloy composition optimization, identifying novel alloys with targeted properties. Multi-objective BO acquisition functions like TSEMO, parEGO, and qNEHVI are benchmarked for efficiency and robustness. To accelerate development, an ICME framework integrates CALPHAD-based simulations for phase stability and thermodynamic predictions, autonomously selecting acquisition functions based on material properties and objectives. Design of Experiments (DOE) strategies minimize validation iterations, reducing timelines, while pool-based active learning enables concurrent computational and experimental work in high-throughput environments. Multi-scale modeling correlates microstructure with performance, with uncertainty quantification ensuring robustness. Data-driven machine learning enables real-time predictions and feedback loops, refining optimization iteratively. This framework, combining advanced optimization, simulations, and experimental validation, provides a scalable solution for rapid discovery and qualification of novel alloys.

8:50 AM
An Integrated Computational Material Engineering Approach to Characterize Mechanical Properties of Cu-Ni-Cr Alloys for Advanced Manufacturing: Pouria Nourian¹; Ahsanul Alam Kabhi¹; Md Ashfaq Siddiquee¹; M Shafiqur Rahman¹; ¹Louisiana Tech University
    Cu-Ni-Cr alloys are prized for their strength, corrosion resistance, and adaptability in demanding sectors like aerospace and energy. This study applies an Integrated Computational Material Engineering (ICME) approach, combining molecular dynamics (MD) simulations, finite element analysis (FEA), and machine learning (ML), to discover alloy compositions with optimized mechanical properties. First, MD simulations calculate key properties—elastic modulus, ultimate tensile strength, bulk modulus, shear modulus, and Poisson’s ratio—across various compositions, validated against experimental trends. These data feed into the macroscale FEA for tensile and bending tests, simulating performance under realistic loads. Using these results as optimization objectives, ML identifies compositions predicted to offer superior mechanical balance. This ICME approach significantly accelerates alloy discovery by reducing dependence on physical testing, enabling rapid optimization across a broad compositional space. The findings advance materials design for high-performance applications, providing a scalable framework adaptable to diverse alloy systems.

9:10 AM
Inverse Design by the MInt System Implementing Materials Integration: Masahiko Demura¹; Satoshi Minamoto¹; Takuya Kadohira¹; Kaita Ito¹; ¹National Institute for Materials Science
    The MInt system embodies the concept of Materials Integration, linking processing, structure, property, and performance in a unified computational framework. By integrating this system with optimization algorithms, such as artificial intelligence, materials and processes can be designed based on targeted performance requirements. This study demonstrates the effectiveness of this approach through two case studies: the design of aging heat treatment processes for nickel-based dual-phase alloys and the optimization of welding processes to mitigate creep life degradation in heat-resistant steel welded joints. These examples highlight how the MInt system enables inverse design, offering a powerful tool for material development and process optimization.