Conference Logo ProgramMaster Logo
Conference Tools for 2027 TMS Annual Meeting & Exhibition
Login
Register as a New User
Help
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools

About this Abstract
Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title Uncertainty-Guided Active Learning for Autonomous Spall Strength Experimentation
Author(s) Ali K. Shargh, Piyush Wanchoo, Ashwini Gupta, Lori Graham-Brady, KT Ramesh, Christopher Stiles, Jaafar El-Awady
On-Site Speaker (Planned) Ali K. Shargh
Abstract Scope Spallation is a dynamic failure process that limits material performance under extreme loading. Developing materials with improved spall resistance is critical for defense, aerospace, energy, and manufacturing applications, yet spall experiments are destructive, expensive, and difficult to scale across broad composition and processing spaces. We present a probabilistic machine learning framework for predicting spall strength using Gaussian Process Regression trained on a curated database of approximately 300 literature-reported spall experiments. The model employs nine physically motivated descriptors to predict spall strength with associated uncertainty estimates. These uncertainties guide active learning to identify the most informative experiments while minimizing experimental effort. Integrated with the Artificial Intelligence for Materials Design Laboratory (AIMD-L) at Johns Hopkins University, the framework enables AI-driven closed-loop experiment selection. Demonstrated on multiple alloy systems, including Ti- and Al-based alloys, the approach illustrates how uncertainty-aware machine learning can accelerate data-efficient materials discovery for dynamic loading applications.
Proceedings Inclusion? Planned:
Keywords Machine Learning, Mechanical Properties, Other

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Comparative Study of Ensemble Machine Learning Models for Nanoindentation Hardness and Elastic Modulus of Ion-irradiated HEAs
A High throughput experimental-computational approach linking Stacking Fault Energy and nanotwin formation in Nickel-based thin film libraries
A Multidimensional Continuous Conditional Generative Adversarial Network to Link Composition-Process-Microstructure-Properties in Mg Alloys
A Paired Benchmark of Tight-Binding and Graph Neural Networks for Electronic Bandgap Prediction
A Physics-Informed Inverse Design Pipeline for Halogen-Free Fire-Retardant Polymers
A Self-Driving Additive Manufacturing Platform for Autonomous Superalloy Design: A case study on Co-superalloys
Accelerating Structural Materials Discovery by Orders of Magnitude Using Random Libraries
Advancing BEE-NET for Physics-Informed Quantum Materials Discovery
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
Any Data is AI-Ready via Semantic Knowledge Graphs
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
COD'HEM: An Integrated Experimental–DFT Materials Informatics Platform for Data-Driven Discovery of High Entropy Materials
Damage assessment digital twin for fusion plasma-facing materials
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
Learning topological evolution in polycrystalline microstructures
Machine-Learned Interatomic Potentials for High-Throughput Screening of Ag-Exchanged Zeolites for Xenon Capture in Nuclear Non-Proliferation Sensing
Machine learning and high-throughput computational guided development of high temperature oxidation-resisting Ni-Co-Cr-Al-Fe based high-entropy alloys
Machine Learning Framework for Predicting and Interpreting Corrosion Behavior of Zinc Alloys
Machine Learning Interatomic Potential for Titanium-Helium systems
Machine learning to derive effective transport properties of multiphase polycrystalline systems
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
Physics-Informed Constitutive Calibration as a Data Problem: From Quasi-Static-to-High-Rate Experiments and FEA Validation
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
Steel S-N curve prediction via domain knowledge guided LLM-assisted symbolic regression
The Surface Problem in Machine-Learned Interatomic Potentials
Thermal properties dataset of high-emissivity ceramic materials generated with automated data extraction
Toward an Autonomous Laboratory for Accelerated Alloy Discovery
Toward Closed-Loop Materials Design: AI Prediction, Uncertainty, and Decision-Making
Trustworthy, Uncertainty-Aware Multimodal Machine Learning for Materials Discovery: Interpretable, Self-Curating Screening of Solid-State Electrolytes
Uncertainty-Guided Active Learning for Autonomous Spall Strength Experimentation
User-Friendly CALPHAD Workflows for Materials Discovery Beyond the CALPHAD Community
XRDReader: Turning Published XRD Information into Machine-Readable Experimental Databases with Multimodal Agentic AI
Questions about ProgramMaster? Contact programming@programmaster.org | TMS Privacy Policy | Accessibility Statement