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Meeting TMS Specialty Congress 2025
Symposium 3rd World Congress on Artificial Intelligence in Materials & Manufacturing (AIM 2025)
Organizer(s) Remi Dingreville, Sandia National Laboratories
Ali Riza Durmaz, Fraunhofer Institute Iwm
Scope The 3rd World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2025) marks a significant milestone in advancing the role of artificial intelligence (AI) within materials science, engineering, and manufacturing. Building upon the success of its predecessors, AIM 2025 gathers stakeholders from academia, industry, and government to delve into the integration of AI in research and manufacturing. The congress aims to address critical issues and chart future pathways for AI implementation, fostering collaboration and innovation in the field of materials science and engineering.

The 3rd World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2025) is a part of TMS Specialty Congress 2025 and featured along with two other co-located events:

-The 8th World Congress on Integrated Computational Materials Engineering (ICME 2025)
-The 7th International Congress on 3D Materials Science (3DMS2025)

Explore your technical interest in a focused, small event environment, while also having access to cross-disciplinary learning and collaboration opportunities with aligned materials communities.
Submit an Abstract Today by the extended deadline of December 13, 2024.

Abstracts are requested on AI topics related to materials science and engineering and manufacturing processes, including:

AI for Workforce Development and Education
Applied ML for Manufacturing (Additive, Architected Materials, Ceramics, Batteries, and Energy Storage)
Autonomous Self-Driving Laboratories
Development of Novel ML Methodologies: Physics-Informed ML, Scientific ML, Graph-based ML
Digital Twins for Manufacturing
Fair Data for Materials and Manufacturing
Optimization of Manufacturing Processes
Large Language Models for Materials and Manufacturing
Abstracts Due 12/13/2024
Proceedings Plan Undecided
PRESENTATIONS APPROVED FOR THIS SYMPOSIUM INCLUDE

3D Surrogate Modeling of Elasto-Viscoplastic FFT Simulations for Porosity-Driven Fatigue Prediction in Additive Manufacturing
A Coupled Thermal-Mechanical Deep Material Network
A Data-Driven Approach to Print Performance Prediction
A Study Into The Generation and Usability of Synthetic Thermodynamic Data for Optimising the Manufacturing of Dual Phase Steels
A Study on Digital Tools for the Safe and Sustainable Design of Materials
A Thermodynamically Consistent Neural Ordinary Differential Equation for Constitutive Modeling of Polycrystalline Metals
Active Learning for Rapid Targeted Manufacturing of Thermoelectric Thin Film Alloys
Adapting to Uncontrolled Variables in Additive Manufacturing Systems
Advancing Data-Driven Uncertainties and Predictions of the Stress Strain Response of Polycrystalline Alloys
Advancing Materials Discovery and Manufacturing Optimization Through Large Language Models
AI-Ready Manufacturing Data for Cybersecurity
AI-Simulation Workflow to Accelerate Computational Discovery of Graphitization Product of Detonation Nanodiamonds
AI Guided Discovery of Lunar Derived Materials for a Sustainable Ecosystem
Analysis of an Intelligent Characteristics of the Blast Furnace Peripheral Zone
ANN-Based Prediction of Steel Hardenability
Application of Deep Learning Approaches to Model the Heat Treatment Process-Microstructure-Property Relationship
Application of LLMs in Understanding Advanced Materials Properties and Manufacturing Processes
Application of ML to Ceramics Industry
Automated 3D Segmentation of Refractory Material Microstructures Using Deep Learning for Improved Corrosion Resistance
Bayesian Calibration and Uncertainty Quantification of a Cohesive Zone Model for Metal-Oxide Interfaces
Bayesian Data Assimilation in Latent Space for Phase-Field Simulation Using Variational Autoencoder
Beyond Bespoke Models: Foundational Vision Transformers for Microstructure Representation and Machine Learning of Microstructure-Property Relationships in Alloys
Causality and Saliency in Unimodal and Multimodal Datasets
Chemical and Materials Informatics for Rapid Toxin-Free Product Development
Correlation of ULTEM 9085 Physical, Chemical, and Mechanical Properties
Data-Driven Design of Architectured Materials: Enhancing Specific Stiffness With Multi-Objective Optimization
Data-Driven Prediction of Deposit Geometry in Air-Based Cold Spray Manufacturing
Data Driven and High Fidelity Modeling Approaches to Advance Understanding and TRL Level of 3D Printing
Deep Learning for Industrial-Scale Modeling of the Basic Oxygen Furnace Process
Detection of Keyhole-Pore Formation in Laser Powder Bed Fusion Using Spatio-Temporal Graph Convolutional Networks
Development of a Massively Parallel Reduced-Order Model Based Design/Optimization Tool for Power Generation Using Natural Gas-H2 Blended Fuels
Development of a Microstructure Image Generation Technique and Machine Learning Model for Property Prediction of As-Cast Materials
Development of Milling State Assessment System Using Machined Surface Images and Engineer's Sensory Evaluations as Supervised Data
Effects of Raster Angle on the Properties of Fused Filament Fabricated Conductive Particles Reinforced Thermoplastic Polyurethane Composite and Prediction of Properties Using Artificial Neural Network
Enhanced Resolution and Image Contrast in 3D XRM Data Using Deep Learning Based Reconstruction Methods
Enhancing Data Acquisition in Manufacturing: Leveraging LLMs for Effective Material Property Databases
Estimation of SOC in Electric Vehicle Batteries Using Machine Learning Models
Fatigue Life and Fatigue Crack Growth Rate Prediction of Additively Manufactured Al 2024 Alloy Using Generative AI and Machine Learning Models
Generative Priors for Regularizing Ill-Posed Problems: Applications to 3D Polycrystalline RVE's
Harnessing AI for Precision Manufacturing of Nanofibers Membranes
Harnessing Large Language Models for Information Extraction and Material Design
Hierarchical Bayesian Modeling for Enhanced Contamination Detection in Electron Beam Powder Bed Fusion Processes
High-Fidelity Grain Growth Modeling: Leveraging Deep Learning for Fast Computations
Innovative AI Method for Predicting Grain Size Distribution During Grain Growth Mechanism
Leveraging CALPHAD to Overcome Data Challenges in Machine Learning for Materials Science
Leveraging Generative Models to Optimize Steel Alloys From Recycled Materials
LLM-Assisted Data Curation in Starrydata: An Open Database of Material Properties Extracted From Published Plots
Long-Term Durability of GFRP Rebars in the Alkaline Environment Under Sustained Loading Using Machine Learning and Empirical Modelling
Machine Learning-Based Prediction of Diffusion Coefficients in Alloys
Machine Learning-Based Prediction of Temperature Fields in Synchronized Multi-Laser Powder Bed Additive Manufacturing (S-MLAM)
Machine Learning-Driven Discovery of High-Hardness Multi-Principal Element Alloys With Physics Informed Priors
Machine Learning-Driven High-Throughput Screening for Optimal Lithium-Ion Battery Electrolyte Solvents
Machine Learning and Molecular Dynamics Simulations Aided Insights Into Condensate Ring Formation in Laser Spot Welding
Machine Learning Applications in Manufacturing Operations Environment
Machine Learning of Mode Filter Grain Growth Model Characteristics
MatCHMaker: Machine Learning for Microstructural Analysis in Materials Characterization Modeling
Metallographic Image and Mask Generation Based on Denoising Deffusion Probabilistic Models for Improving Metallographic Image Segmentation
Microstructure Statistics and Interpretable Property Prediction in Multifunctional Electrodes Using Random Forests
ML-Based Process Monitoring for Porosity Detection and Rectification in L-PBF of SS316
MLOgraphy++: A Context-Enhanced U-Net Approach for Robust Grain Boundary Segmentation in Metallographic Images
Modern Methods for Understanding the Electrochemical Stability of Inorganic Materials
Multidimensional Analysis for Correlation of Mechano-Physico-Chemical Attributes With Bio-Functionality in Eight TiNbZrSnTa (TNZST) Alloys
Offline Programming for Wire-Arc DED Applications Using Machine Learning Algorithms
On the Use of Decoder-Only Transformers to Model Time-Series based Silicon Data
Ontology-Based Digital Representations of Materials Testing in the MaterialDigital Initiative
Optimization of Machining Conditions for Improved Machining Quality Based on a Digital Twin of Machine Tools
Optimizing Deep Learning Training for Scientific Imaging of Fiber-Reinforced Composites
Out-of-Distribution Surface Anomaly Detection Using Masked Autoencoder Vision Transformers
Performance Analysis of Different Shaped Tool Electrodes During Electrical Discharge Machining of Inconel 718
Physical-Informed Machine Learning for Silicone Formulation Development
Physics-Informed Generative AI for Predicting Material Deformation: Latent Diffusion Modeling From Undeformed Microstructures
Physics-Informed ML for Crystal Growth Manufacturing
Prediction of Creep Life Using K-Means Clustering and Gaussian Process Regression
Process Control System and Cloud Simulation Platform for Continuous Casting Based on Digital Twin Technology
Quantum Machine Learning: Revolutionizing Fatigue Fracture Mode Prediction of Laser Powder Processed Inconel 625
Rapid Bubble and Cavity Tracking Utilizing Machine Learning
ROM-Net in Additive Manufacturing
Salvaging of Materials Fatigue Data from Literature Using Language Model Systems
Simulation-Based Optimization of Additive Manufacturing Toolpaths to Reduce Distortion
Strength in Uncertainty: Federated Learning for Accelerated Innovation
Strongly Physics-Constrained Neural Networks for Mechanical Superresolution
Surrogate Modeling of Cluster Dynamics-Predicted Nucleation and Growth of Irradiation Defects Using Time-Series Neural Networks
Synthetic Microstructure Generation and Prediction Using Stable Diffusion
Texture Evolution Surrogate for Magnesium Materials
The Variational Deep Materials Network: Efficient Extrapolation With Uncertainty of Homogenized Material Responses
Topological Analysis of Processing to Microstructure Mappings
Transfer Learning for Nanomaterial Classification
Transfer Learning for Ultrasonic Crystallography: Accelerating Orientation Mapping in Materials With Neural Networks
Understanding Manufacturing and Materials Design Spaces: Overcoming Expensive Data
Unsupervised Learning for Low Dimensional Corrosion Quantification of Aluminum Films
Utilizing Large Language Models for Interpreting & Interfacing With Instance Segmentation Results of Metallic Powder Micrographs
Utilizing Time-Series Data for Improved Prediction of End-Point Temperature and Carbon in Basic Oxygen Furnace With a Large Industrial Dataset
Physically Aligned Hierarchical Mesh-Based Network for Dynamic System Simulation

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