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About this Symposium

Meeting 2027 TMS Annual Meeting & Exhibition
Symposium Computational Discovery and Design of Materials
Sponsorship TMS Materials Processing and Manufacturing Division
TMS: Computational Materials Science and Engineering Committee
Organizer(s) Sara Kadkhodaei, University of Illinois Chicago
Yong-Jie Hu, Drexel University
Yanqing Su, University of Oklahoma
Wennie Wang, University of Texas at Austin
Xiaofeng Qian, Texas A&M University
Satyajit Mojumder, Washington State University
Suhas Eswarappa Prameela, University of Utah
Scope Recent advances in first-principles electronic-structure methods and high-performance computing are enabling predictive discovery and mechanistic understanding of emerging materials for technologically important applications. In particular, quantum mechanical approaches based on density functional theory (DFT) and beyond-DFT techniques have become central tools for uncovering fundamental mechanisms governing structure, stability, and functional properties of complex materials systems. This symposium focuses on the use of physics-based computational methods, particularly first-principles and atomistic simulation approaches, to discover, design, and understand materials from the electronic to atomic scales. Emphasis is placed on rigorous theoretical modeling and predictive simulations that provide mechanistic insight into materials behavior, including defect physics, phase stability, interfacial phenomena, and emergent functional properties. Contributions that integrate first-principles modeling with targeted experiments to validate predictions and reveal new physical mechanisms are especially encouraged. While data-driven or machine learning methods may be included where they support or accelerate physics-based modeling workflows, the primary focus of the symposium is on fundamental computational approaches grounded in electronic structure theory and atomistic simulation.

The symposium will bring together researchers working on the development and application of first-principles computational methods to advance understanding and discovery of functional, energy, and quantum materials.

Topics of interest include, but are not limited to:

• First-principles modeling of electronic, magnetic, optical, and quantum properties of emerging materials
• First-principles design of functional materials including superconductors, quantum materials, catalysts, and energy-storage materials
• Ab initio prediction of phase stability, defect energetics, and thermodynamic properties
• First-principles studies of surfaces, interfaces, and heterostructures
• Atomistic simulations of defects, disorder, diffusion, and microstructural evolution
• Rare-event and accelerated dynamics methods for phase transformations and degradation mechanisms
• First-principles investigations of materials for extreme environments and reliability-critical applications
• Integration of ab initio simulations with targeted experimental validation
• Physics-guided computational screening and discovery of novel materials systems
• Data-assisted acceleration of first-principles workflows (e.g., surrogate potentials or limited machine-learning approaches supporting ab initio simulations)

Abstracts Due 07/15/2026
Proceedings Plan Planned:

PRESENTATIONS APPROVED FOR THIS SYMPOSIUM INCLUDE


A Multi-Fidelity Machine-Learning Workflow for Accelerated Lithium-Ion Cathode Materials Development
Accelerating Quantum Materials Discovery with AI
Agapi-agents: An open-access agentic ai platform for accelerated materials design on atomgpt. org
Assessing, training, and fine-tuning foundation potentials for plasticity-relevant properties of metals
Beyond First Principles: Convergent and Thermodynamically Self-Consistent Atomistic Phase Diagrams from Interatomic Potentials
Capturing Spin-Disorder in Paramagnetic Austenitic Steels: A First-Principles Approach
Characterization of the Atomic-Scale Strengthening Mechanisms in Oxide Dispersion Strengthened Alloys at Elevated Temperatures
Closing the Loop on Superconductor Discovery
Collective motion of screw dislocations in α-Fe and its effect on Peierls stress
Computational discovery of synthesizable and hard multicomponent transition metal carbides
Crystal Structure Exploration Under Extreme Conditions
Defect-Controlled Carbon Transport in Zirconium Carbide under Charge, Temperature, and Pressure Extremes.
Digital material design guidelines for mitigation of alloy embrittlement
Effects of Chemical Fluctuations and Short-Range Order on Dislocation Behavior in Multi-Principal-Element Alloys
Enhancing of the Thermal Conductivity in Refractory-based High Entropy Alloys via Mott s-d Scattering Mechanism
ExactBO: GPU-Accelerated Certified Bayesian Optimization for Continuous and Constrained Materials Design
First-Principles and Graph Learning Framework for Discovery of Li-Ion Transport Pathways in Solid Electrolyte Interphases
First-principles mapping of defect energetics across the 211 MAX phase chemical space
First-Principles Transport in AlGaN/GaN HEMT-like Interfaces
First Principles Data Assisted Investigation of Spin Ordering in Spinel Ni1−xZnxFe2O4 Crystal
From DFT Defect Mechanisms to Transport-Scale Modeling of Hydrogen Isotopes in Li2O
Geometric Design of d- and i-wave Altermagnetism in Graphene Nanohole Superlattices
Humans-(less)-in-the-loop: moving toward autonomous materials design for metal-organic frameworks
Hysteresis in silicon-dominated anodes in lithium-ion batteries.
Integrating Physical Models, Specialized Databases, AI/ML Tools, and Self-Driving Labs to Understand the Synthesis of Inorganic Materials
Intermetallic Composition as a Variable in Alloy Design
Machine Learning-Assisted Design of High-Performance HOR Catalysts for Proton-Conducting Fuel Cells
Machine Learning Screening of Oxide Semiconductors for Energy-Efficient Back-End Transistors
Metadynamics-Accelerated Active Learning of Interatomic Potentials for InP Magic-Sized Cluster Nucleation
PhaseForge: Automated, Uncertainty-Quantified Construction of CALPHAD Thermodynamic Databases via Machine-Learning Interatomic Potentials and Bayesian Parameter Refinement
Physics-Grounded AI for Materials Discovery
Physics-Informed Discovery of Hydride Superconductors
Predicting plateau slope in metal hydrides from first-principles: hydrogen energy and charge distributions in TiMn2-based alloys
Prediction and Screening of Transition Metal Nitrides for Ammonia Synthesis
Quantifying the Contributions of Strengthening Mechanisms in Refractory Complex Concentrated Alloys through Physics-Informed Statistical Learning.
Role of a Ni₁₃ Cocatalyst in Promoting CO₂ Activation and Hydrogenation on CaCu₃Ti₄O₁₂: A First-Principles Study
Size Effects on Chemical Ordering in High-Entropy MXenes: A First-Principles Validated Atomistic Study
Stabilizing Titanium Carbide MXenes in Water with Montmorillonite Heterostructures: Ab Initio Molecular Dynamics Study
Thermodynamic models for the epitaxial growth of oxide solid solutions
Understanding Point Defects in Semiconductors using First Principles Simulations Accelerated by ML Potentials


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