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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 Areas of Agentic Leverage in Computational Materials Science
Author(s) Steven Torrisi
On-Site Speaker (Planned) Steven Torrisi
Abstract Scope I will detail recent work on the application of AI agents to a variety of tasks in computational materials science, ranging from the setup and execution of calculations to the autonomous discovery of new materials. Drawing on ongoing work in industrial settings, I will showcase particular areas of application and specify how and why agents are most useful in each.
Proceedings Inclusion? Planned:
Keywords Modeling and Simulation,

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 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
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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
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Assessing Uncertainty and Reproducibility in ML-Based Grain Growth Predictions: Impact of Training Data
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Data-efficient Deep Generative Modeling for Microstructure Prediction under Unexplored Compositional Conditions
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Design of Experiments for Efficient XRF Under Compositional Uncertainty
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Development of Machine-Learning Interatomic Potential forSolute–Dislocation Interactions in Fe–C–Cu Steels
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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
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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
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Mag-GPT: From Query to Magnetic Materials Discovery
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Multi-Scale Microstructure–Property Prediction Using Deep Learning
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