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About this Abstract
Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title Toward Closed-Loop Materials Design: AI Prediction, Uncertainty, and Decision-Making
Author(s) Jaafar A. El-Awady
On-Site Speaker (Planned) Jaafar A. El-Awady
Abstract Scope Artificial intelligence and machine learning are increasingly enabling materials design workflows that move beyond forward prediction toward decision-making. In this talk, we will describe our efforts to integrate AI into the Artificial Intelligence for Materials Design Laboratory at JHU as a foundation for closed-loop discovery of structural materials. The workflow links phase prediction, quasi-static strength prediction, and spall strength prediction with high-throughput experimentation and uncertainty-aware modeling. Rather than using AI only to predict properties, we use model uncertainty to support active learning, prioritize the next most informative experiments, and guide decisions across composition, microstructure, and properties. This approach moves the laboratory from trial-and-error exploration toward AI-guided experimental planning. The presentation will highlight how integrated data infrastructure, physics-informed machine learning, uncertainty quantification, and high-throughput validation can help build the foundations for future autonomous materials discovery in demanding environments.
Proceedings Inclusion? Planned:
Keywords ICME, Machine Learning, Other

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

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A Multidimensional Continuous Conditional Generative Adversarial Network to Link Composition-Process-Microstructure-Properties in Mg Alloys
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A Self-Driving Additive Manufacturing Platform for Autonomous Superalloy Design: A case study on Co-superalloys
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Agentic AI-Driven Design of Refractory Multi-Principal Element Alloys
Agentic AI for Assessing and Optimizing Scientific Reasoning and Creativity in Chemical Vapor Deposition Diamond Synthesis
Agentic Lifestyles in the Era of AI: Research Workflows and Insights from the Vertical Cloud Lab @ BYU
Agentic Resource Allocation for Batch Multi-Objective Bayesian Optimization in Autonomous Materials Discovery
AI-driven theoretical frameworks to determine structure of complex materials and defects from experiments
AI-Enabled High Throughput Discovery of High Entropy Alloys for Extreme Environments
An Automated Probabilistic Machine Learning Framework for Materials Modeling and Active Learning: Benchmarking Uncertainty Quantification Across Analytical, Computational, and Experimental Domains
An informatics approach to predict shape memory effect in CrMnFeCoNi high-entropy alloys
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Areas of Agentic Leverage in Computational Materials Science
Assessing Uncertainty and Reproducibility in ML-Based Grain Growth Predictions: Impact of Training Data
Automating LabDCT Workflows via Intelligent, Data-Driven Guidance
CALIBER: A Retrieval-Augmented, Uncertainty-Aware Platform for Selecting High-Fidelity Characterization Parameters for Novel Alloys
Causally Informed Machine Learning for Nanoporous Microstructure-Property Relationships
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Data-driven inference of twin network dynamics: nucleation, co-nucleation, transmission, and incidental contacts
Data-Driven Surrogate to FE for Predicting Elastoplastic Constitutive Relation in Polymer Nanocomposites
Data-Driven Tensile Strength Prediction of Biodegradable Zinc Alloys
Data-efficient Deep Generative Modeling for Microstructure Prediction under Unexplored Compositional Conditions
Deep learning based optimization of hydrogels and their funcationalization for applications in ophthalmology
Design of Experiments for Efficient XRF Under Compositional Uncertainty
Developing Methods, Models, and Datasets for a Self-Driving Magnetic Nanomaterials Laboratory: Application for Thermal Magnetic Particle Imaging
Development of Machine-Learning Interatomic Potential forSolute–Dislocation Interactions in Fe–C–Cu Steels
Dis-GNN: Crystal Graph Neural Network for Disordered Crystal Classification and Site Occupancy Prediction
Dual Machine Learning Discovery of the Radius of Informative Structural Environments in Metallic Glasses
Fine–Tuned Universal Machine Learned Interatomic Potentials for Surface Property Prediction
From Loading to Architecture: A Physics-Guided Machine-Learning Framework for Polymer Spall Strength
Graph Neural Network Optimization of Industrial Materials, Formulations, and Chemical Processes
Graph Neural Network Prediction of Grain-Boundary Diffusion from a Reduced Active-Atom Subset
Graph Neural Networks for the Prediction of Metastable Grain Boundary Dynamics
Hyperparameter-Informed Sparse Bayesian Active Learning for Understanding and Controlling Frontal Radical Polymerization
Integrating AI-enabled analysis and agentic workflows at scientific user facilities
Interpretable Machine Learning and Topological Descriptors for Structural Evolution, Interfaces, and Dopant Thermodynamics: A Halide Perovskites Case Study
Interrogating Materials Informatics Models Along Experimentally Relevant Alloying Pathways
Knowledge-Evolution-Informed Forecasting of Mg Alloy Research Using Large Language Models
Label-Efficient Strategies for Small-Data Materials Modeling with a Nuclear Waste Glass Case Study Study
Language-Based Modeling for Additive-Manufacturing Alloy Design: Learning, Reasoning, and Generative Exploration
Learning Across Theories: A Multi-Fidelity Machine-Learned Interatomic Potential for the Energetic Molecular Crystal LLM-105
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Machine Learning Framework for Predicting and Interpreting Corrosion Behavior of Zinc Alloys
Machine Learning Interatomic Potential for Titanium-Helium systems
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Mag-GPT: From Query to Magnetic Materials Discovery
Million-scale Surface Property Screening Enabled by Universal Machine Learning Interatomic Potentials
Multi-Scale Microstructure–Property Prediction Using Deep Learning
Multi-Surrogate Strategies for Multi-Objective Bayesian Optimization: A Cross-Phase Empirical Study from Static Baselines to LLM-Proposed Policies
Multiscale Modeling of GaN/AlGaN Interfaces with Machine Learning and DFT
Out-of-Distribution Phase Equilibrium Prediction Using Physics-Informed Graph Attention Networks
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Physics-Informed Graph Neural Networks for Predicting Li-Ion Transport in Solid Electrolyte Interphases
Physics-informed Machine Learning Framework for Material Parameters Estimation from Materials Morphology
Physics-Informed Machine Learning Framework for Predicting Electromigration Failure in Solder Joints
Physics Informed Neural Network Recovery of Continuous Relaxation Time Spectra in Linear Viscoelasticity
Process-aware Extraction of Composition–processing–property Records from Materials Literature Using Large Language Models
Research on Molten Steel Carbon Content Prediction for Multi-Procedure Steelmaking Process
Resource-Constrained AI Discovery of Manufacturable Wear-Resistant Steels
Self-Supervised Learning of Materials States Evolution from Microscopy Data Using Joint Embedding Predictive Architectures
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