A Hierarchical Synergistic Deep Learning Framework Integrating Composition, Structure, and Ionic Transport for Solid-State Electrolyte Discovery

arXiv:2608.25592 · cond-mat.mtrl-sci, cs.AI, cs.LG · Submitted 2026-08-26 · Read on arXiv

cond-mat.mtrl-sci, cs.AI, cs.LG

Submitted: 2026-08-26

Updated: 2026-08-26

Code: https://github.com/dhw059/MatCascade

Terminology

• Benchmark — 20× in this paper · explained in 2 episodes
• Accuracy — 19× in this paper · explained in 1 episode
• Fidelity — 14× in this paper · explained in 2 episodes
• Consistency — 12× in this paper · explained in 3 episodes
• Generalization — 7× in this paper · explained in 13 episodes
• Adam — 4× in this paper · explained in 2 episodes
• Core — 4× in this paper · explained in 2 episodes
• Materials Project — 4× in this paper
• Benchmarking — 3× in this paper · explained in 4 episodes
• DFT Calculations — 3× in this paper · explained in 2 episodes
• GNNs — 3× in this paper · explained in 2 episodes
• Open Quantum Materials Database — 3× in this paper
• Ab Initio — 2× in this paper · explained in 1 episode
• Active Learning — 2× in this paper · explained in 6 episodes
• Data Fusion — 2× in this paper · explained in 3 episodes
• Metastability — 2× in this paper · explained in 3 episodes
• Supporting Information — 2× in this paper
• Verification — 2× in this paper · explained in 2 episodes
• Abundance — 1× in this paper · explained in 1 episode
• Analyzer — 1× in this paper · explained in 1 episode
• Applicability Domain — 1× in this paper · explained in 1 episode
• Commentary — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Feature Selection — 1× in this paper · explained in 4 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Frustration — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Graph Neural Networks (GNNs) — 1× in this paper · explained in 7 episodes
• Hierarchical Framework — 1× in this paper · explained in 2 episodes
• Phase Transitions — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Transferability — 1× in this paper · explained in 4 episodes

Sources

• MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures
• Orb: A Fast, Scalable Neural Network Potential

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