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Meeting

2027 TMS Annual Meeting & Exhibition

Symposium

Computational Discovery and Design of Materials

Presentation Title

Accelerating Quantum Materials Discovery with AI

Author(s)

Trevor David Rhone

On-Site Speaker (Planned)

Trevor David Rhone

Abstract Scope

van der Waals (vdW) materials provide a versatile platform for emergent quantum phenomena, including long-range magnetic and topological order. With trillions of possible vdW materials and heterostructures, AI offers a powerful route to accelerate their discovery. In this talk, we present an AI-driven framework for exploring the vast chemical space of vdW materials and identifying compounds with desirable spin and charge properties. We focus on A₂X₆ crystal structures derived from monolayer Cr₂I₆ and investigate their magnetic properties and formation energies using density functional theory (DFT) combined with a foundation-inspired materials model trained on large-scale unlabeled data and limited property labels. We also examine MnBi₂Te₄/Sb₂Te₃ heterostructures to demonstrate the broader applicability of this approach. Our results show that combining AI with DFT enables efficient prediction of vdW material properties, paving the way for the rapid discovery of chemically stable quantum materials for spintronics and emerging quantum computing technologies.

Proceedings Inclusion?

Planned:

Keywords

Magnetic Materials, Machine Learning, Computational Materials Science & Engineering

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

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Beyond First Principles: Convergent and Thermodynamically Self-Consistent Atomistic Phase Diagrams from Interatomic Potentials

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Characterization of the Atomic-Scale Strengthening Mechanisms in Oxide Dispersion Strengthened Alloys at Elevated Temperatures

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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

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First-Principles Transport in AlGaN/GaN HEMT-like Interfaces

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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.

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Understanding Point Defects in Semiconductors using First Principles Simulations Accelerated by ML Potentials

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