World Congress on Artificial Intelligence in Materials and Manufacturing on July 06-10, 2026 in Anaheim, United States

Technical topics include, but are not limited to:

Applied AI for Manufacturing

• Optimizing Fabrication Processes (Material Synthesis, Additive Manufacturing/Subtractive Material Processing, Thermo-Mechanical Treatments) and Product Quality
• Digital Twins for Smart Manufacturing
• Fusion of Process and Sensor Data for Product Performance Assessment and Predictive Maintenance

Physics-Grounded AI for Multi-Scale and/or Multi-Objective Materials Modeling

• Physics-Informed Neural Networks (PINNs) for Modelling Material Behavior
• Hybrid AI Frameworks for Predicting Material/Component Behavior (e.g., Integration of Finite Element and AI Methodologies)
• AI-Based Multi-Objective Optimization in Materials Design

AI-Driven Materials Discovery & Design

• Inverse Design of Structural and Functional Materials Using Deep Generative Models
• AI-Enhanced Process-Structure-Property Modelling
• Reinforcement Learning for Autonomous Materials Discovery
• Graph Neural Networks for Predicting Properties of Compounds and Microstructures

Sustainable & Green Materials via AI

• AI-Guided Materials Discovery for Eco-Friendly Substitutes to Critical Elements and Materials (e.g., Rare-Earth Elements, Forever Chemicals)
• Data-Driven Design of Materials for Circularity
• Discovery of Novel Energy Materials (Catalyst, Magnetic, Battery, Solar Materials)
• Process Optimization for Product Carbon Footprint Reduction in Industry

AI-Assisted Self-Driving Laboratories

• AI-Guided Closed-Loop Orchestration of Autonomous Laboratories
• Generative AI Methodologies for Sampling Candidate Materials (e.g., Bayesian Optimization, Genetic Algorithms, Active Learning)
• AI-Based Robot Control Methodologies for High-Throughput Materials Synthesis/Characterization (Reinforcement Learning, Telemetry Methodologies, Vision-Based Control)
• Ethical Implications of AI and Autonomous Systems in Materials Research - Chances and Risks
• Human-Machine Interaction and Human-In-The-Loop Systems for Collaborative AI in Materials Discovery

AI for Management, Curation, and Enrichment of Materials Data

• FAIR Materials Datasets (Findable, Accessible, Interoperable, Reusable)
• Open Platforms and Datasets for Benchmarking Materials-Related Tasks
• Ontology Development for Materials Knowledge Bases
• Data Integration Using Materials and Manufacturing Knowledge Graphs
• AI-Based Data Quality and Quantity Enhancements (e.g., Data Fusion Techniques for Multi-Modal Materials Data, Superresolution Approaches, Artifact Reduction, Synthetic Data Generation)

Computer Vision for Materials Science and Engineering

• Microstructure Characterization and Reconstruction Using Image-Based Techniques
• Deep Learning for Microstructure-Based Property Prediction and Modeling
• AI-Powered Quality Control and Defect Detection in Manufacturing

Large (Vision and Reasoning) Language Models (LLMs/VLMs/RLMs) for Materials and Manufacturing

• Large (Vision) Language Models for Structured Information Extraction from Scientific Literature and Patents
• Multimodal AI Models (Utilizing a Combination of Text, Image, Video, Time-Series, etc. Modalities)
• Development of Agentic Language Model Systems (e.g., for Materials Design, Workflow Orchestration, Question-Answering Systems, Literature Research)
• AI-Enabled Ontology Learning and Population for Materials Knowledge Bases
• Application and Reinforcement Learning of Reasoning Language Models in the Materials Domain

Foundational Models in Materials Informatics

• Development of Materials Foundational Models (Self-Supervised or Reinforcement Learning)
• Evaluation of Foundational Models for Materials-Related Tasks
• Strategies for Improved Model Generalization Across Material Classes (Transfer Learning/Domain-Adaptation/Zero-Shot Learning Techniques)
• Data-Frugal Methodologies for Addressing Data Scarcity

Materials Data Mining and Extraction of Causal or Symbolic Relationships

• Interpretability Methods and Explainable AI (XAI)
• Causal Learning
• Symbolic Regression (e.g., for Learning Constitutive Models)
• Neurosymbolic AI
• Uncertainty Quantification in AI-Driven Materials Modeling

Name: The Minerals, Metals & Materials Society (TMS)
Website: http://www.tms.org
Address: 5700 Corporate Drive Suite 750

The Minerals, Metals & Materials Society (TMS) is a professional society that connects minerals, metals, and materials scientists and engineers who work in industry, academia, and government positions around the world. We create networking, publication, and professional development opportunities by convening international conferences, publishing books and journals, administering awards, conducting short courses and training, and bringing together the professional community to address issues of common concern. We also provide leadership in the accreditation of university programs in metallurgical, materials, and similarly named engineering programs.