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About this Symposium
Meeting TMS Specialty Congress 2026
Symposium 4th World Congress on Artificial Intelligence in Materials & Manufacturing (AIM 2026)
Organizer(s) Ali Riza Durmaz, Fraunhofer Institute Iwm
Marat I. Latypov, University of Arizona
Scope The 4th World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2026) 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 2026 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 4th World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2026) is a part of TMS Specialty Congress 2026 and featured along with three other co-located events:

-The 4th World Congress on High Entropy Alloys (HEA 2026)
-The inaugural 1st World Congress on Reproducibility, Qualification, and Standards Development of Additive Manufacturing and Beyond (RQSD 2026)
-CSM-TMS Energy Materials 2026

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 January 5, 2026.

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

Applied AI for Manufacturing
Physics-Grounded AI for Multi-scale and/or Mult-objective Materials Modeling
AI-Driven Materials Discovery & Design
Sustainable & Green Materials via AI
AI-Assisted Self-Driving Laboratories
AI for Management, Curation, and Enrichment of Materials Data
Computer Vision for Materials Science and Engineering
Large (Vision and Reasoning) Language Models (LLMs/VLMs/RLMs) for Materials and Manufacturing
Foundational Models in Materials Informatics
Materials Data Mining and Extraction of Causal or Symbolic Relationships

See the official conference website at www.tms.org/specialtycongress2026/AIM2026 for further sub-topic details.
Abstracts Due 01/05/2026
Proceedings Plan Undecided

PRESENTATIONS APPROVED FOR THIS SYMPOSIUM INCLUDE

3D PRIMME for Learning Grain Growth Behaviors from Simulated and Experimental Datasets
A Reinforcement Learning Approach to Alloy Design for Energy Materials
Accelerated Prediction of EAF Melting Processes Through an Xgboost-Based Reduced-Order Model
Accelerating Design-Plan-Print-Qualify Workflows for Additive Manufacturing with Agentic AI
Adaption to Uncontrolled Variables in Additive Manufacturing
AFM-net: From Scarce Data to Fast Scans. Machine Learning Acceleration of AFM Nanometrology
AI-Driven Alloy Design Framework for Cost-Effective Ultrahigh Strength Martensitic Steels
AI-Driven Raman–Terahertz System for Real-Time Measurement of Polymer Crystallinity
AI-Enabled Microstructure Characterization of an Al–Si–Mg–Cu Die-Casting Alloy Under 490 °C Solution Treatment
ANN Based Modeling of Thermodynamic, Transport, and Transformation Related Properties
Automated Analysis Using Deep Learning for Complex Low-Temperature Transformation Microstructures in Advanced High-Strength Steel
Beyond Trial and Error: Bayesian Paradigms for Intelligent Alloy Design
Chemical-Disorder-Informed Machine Learning Framework for Multi-Objective Design of Refractory Multi-Principal Element Alloys
Data-Driven Discovery of Creep Equations via Symbolic Regression
Diagnosing Mechanical Assembly Discontinuities with LLMs
Empower Finite Element Software to Perform Machine Learning for Modelling Material Behaviours
European Infrastructure Activities on Management of Materials Data for Research, Development and Application of Advanced Materials
Evaluating and Optimizing Large Language Models in Manufacturing Contexts
Extracting Symbolic Relationships in Materials Science and Engineering
From ICME to Industry: A Platform Approach to the Discovery and Commercialization of Advanced Alloys
Generalist to Specialist Sequential Segmentation of Label-Free Low-Contrast Al-Si Solidification Video
High-Fidelity Phase-Field lattice Boltzmann Simulation for Dataset Generation Enabling Machine Learning of Melt Pool Dynamics in PBF
ICME-Driven, Uncertainty-Aware Surrogate Modeling of Composite EV Battery Enclosures Using High-Throughput Simulations and Machine Learning
In-Situ Prediction of 3D-Printed Outcomes
Integrated Generative and Predictive Machine Learning Models for Estimation of Microstructure and Cold Dwell Fatigue Life in Ti-6Al-4V Forgings
Integrated Machine Learning Framework for Quality Prediction of Hot Forging
Leveraging Large Language Models for Alloy Property Prediction: A Review of Regression Approaches
Machine Learning-Assisted Design of Resorbable Magnesium Alloys
Machine Learning-Based Prediction of Mechanical Property Heterogeneity in Inconel 718 Superalloy Manufactured by Directed Energy Deposition
Microstructure-Sensitive Segmentation of γ′ Phase in Superalloys Using Detectron2 for Data-Driven Alloy Design and Process Optimisation
Neural Modeling of Anisotropic Yield Behavior Under Plasticity Constraints
Optimal Resource Utilization for Autonomous Lab Orchestrators
Phsyics-Guided Machine Learning for Predicting Multiscale Failure in Composites
Real-Time Digital Twin for EAF Process Optimization
Semantic Knowledge Graphs for AI-Ready Data
Simulation-Driven Machine Learning for Predicting Material Properties from Cross-Sectional Dendritic Microstructures
Superintelligence for Scientific Discovery: Multi-Agent Swarms and Large Reasoning Models
Towards a Modular Autonomous Research Ecosystem for Materials Science
Unsupervised Microstructure Segmentation of Forged TiAl Alloys Using Machine Learning
Update-Free Reinforcement Learning for Scalable Resilience in Expeditionary Conditions
μSAM-Based Inference of Nuclear Materials Processing History from SEM Imagery

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