Computational Materials Science
The impact of ionic anharmonicity on superconductivity in metal-stuffed B-C clathrates
This research reveals the crucial role of ionic quantum and anharmonic effects in stabilizing metal-stuffed B-C clathrates and enhancing their superconductivity. Utilizing advanced ab initio methods and machine learning, 15 previously unstable materials were identified as dynamically stable, including novel (XY)¹+ state compounds critical for high critical temperatures. Notably, KRbB6C6 and RbB3C3 achieved record-high critical temperatures of 102 K (0 GPa) and 115 K (15 GPa) respectively, surpassing liquid nitrogen temperatures. This underscores the importance of anharmonicity in the search for high-Tc superconductors at near-ambient pressure.
Quantum-Accelerated Material Innovation for Superconducting Technologies
The ability to predict and stabilize novel high-temperature superconductors at accessible pressures has transformative potential for industries like energy, computing, and defense. Our AI-driven platform integrates quantum anharmonicity principles to drastically reduce R&D cycles, enabling the discovery and optimization of next-generation materials faster and more cost-effectively. This accelerates pathways to energy-efficient infrastructure and advanced quantum devices.
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Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
15
New Stable Compounds
Ionic quantum and anharmonic effects enabled dynamic stability for 15 materials previously considered unstable in the harmonic approximation, including unreported (XY)¹+ state compounds, crucial for high critical temperatures.
115
K
Predicted Tc of 102 K for KRbB6C6 (0 GPa) and 115 K for RbB3C3 (15 GPa), both exceeding liquid nitrogen temperatures and KPbB6C6's 92 K at the anharmonic level.
Anharmonic Effects on Vibrational Modes
Quantum and anharmonic effects harden the Eg and Eu vibrational modes, crucial for the dynamic stability of B-C clathrates. This impacts electron-phonon coupling and overall material stability, allowing for the reclassification of unstable materials.
Enterprise Process Flow
DFT-level calculations for anharmonic energy surface (AES) refinement
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Machine Learning Potentials (MLPs) & Active Learning for accelerated simulations
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Stochastic Self-Consistent Harmonic Approximation (SSCHA-ACNN) for dynamic stability
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Anisotropic Migdal-Eliashberg equation for accurate Tc prediction
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Thermodynamic stability analysis for experimental synthesizability
| Feature | Harmonic Approximation (Traditional) | Anharmonic (Quantum-Aware) Approach |
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| Relevance to B-C Clathrates |
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Case Study: Designing High-Tc B-C Clathrates
Problem: Traditional harmonic approximations fail to accurately predict the stability and superconductivity of lightweight B-C clathrates, limiting the search for high-Tc materials at accessible pressures.
Solution: Our platform employs a state-of-the-art SSCHA-ACNN workflow, integrating quantum anharmonicity and machine learning potentials to precisely model lattice dynamics and electron-phonon coupling.
Outcome: Identified KRbB6C6 and RbB3C3 with Tcs up to 115 K, demonstrating the power of quantum-aware AI to overcome limitations of traditional methods and unlock new classes of high-temperature superconductors.
Advanced ROI Calculator
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Estimated Annual Savings
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Accelerating Your Superconductor R&D Roadmap
Our structured approach ensures a seamless integration of AI into your material science workflows, delivering tangible results at every stage.
Phase 1: Quantum Material Assessment
Evaluate current material R&D pipeline, identify key bottlenecks in superconductor discovery, and integrate initial quantum simulation datasets.
Phase 2: AI Model Customization & Training
Configure AI models for specific B-C clathrate or similar material systems, fine-tune machine learning potentials with proprietary data, and validate initial predictions.
Phase 3: High-Throughput Screening & Validation
Utilize AI to rapidly screen potential high-Tc candidates, perform detailed quantum anharmonicity simulations, and cross-reference with experimental validation data (if available).
Phase 4: Scalable Deployment & Continuous Optimization
Integrate the AI platform into full-scale R&D operations, establish continuous learning loops for model refinement, and explore new material classes with enhanced predictive power.
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