The study uses large‑scale, quantum‑accurate reactive simulations powered by active learning and deep equivariant neural network potentials to investigate the coupled reactions and diffusion at the interface of a symmetric solid‑state battery cell. Unsupervised clustering of local atomic environments reveals a previously unreported crystalline disordered phase, Li₂S₀.₇₂P₀.₁₄Cl₀.₁₄, in the solid‑electrolyte interphase (SEI), and explains experimental observations of SEI formation and lithium creep mechanisms that drive dendrite initiation. The work demonstrates a parameter‑free digital twin capable of providing atomistic insights into complex heterogeneous processes in solid‑state electrochemistry.
arXiv:2607. 25597v1 Announce Type: new Abstract: The reactivity of lithium-metal electrolytes arises from the interplay of molecular functional groups, Li$^+$ solvation, and salt-anion participation.
The paper introduces EMolStudio, a density‑matrix‑centered AI platform that predicts and analyzes electronic structure for electrolyte molecules in lithium batteries. It integrates molecular functionalization, explicit Li⁺ first‑shell assembly, density‑matrix prediction, and electronic‑structure parsing, enabling systematic study of 163,655 functionalized molecules and 22,500 first‑shell clusters across four lithium salts. The analysis reveals that functional groups and anion identity distinctly influence frontier orbital energies, electrostatic potential, and Li⁺‑donor contacts, with LiTDI anchoring the highest occupied orbital on the anion.
The article reviews strategies for modeling chemical disorder in materials, addressing the gap between experimental descriptions of disorder and the detailed configurations required for atomistic simulations and AI workflows. It evaluates traditional approaches such as mean-field theories, cluster expansion, and Monte Carlo, alongside emerging AI-powered methods like universal interatomic potentials and generative models. The review also discusses how AI can accelerate computational schemes and enable disorder-native capabilities, providing a roadmap for integrating disorder into realistic AI-accelerated materials discovery.
The paper presents a hierarchical deep‑learning framework that integrates compositional, structural, and transport models to screen solid‑state electrolytes. Four modules—L‑G‑DCNN, DenseGNN, MatterSim, and DeePMD—coordinate to evaluate thermodynamics, multi‑property performance, and kinetic transport, outperforming existing methods. Applied to over 30 million candidates, the workflow identifies 97 high‑performance materials, mainly halides, and links Li⁺ jump‑network connectivity to ionic conductivity while highlighting limits for oxide electrolytes.
arXiv:2606. 28220v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving nonlinear partial differential equations (PDEs), including battery electrochemical models.
The paper introduces a method to construct phase‑field models directly from ab initio data by projecting molecular dynamics onto species‑density fields using the Mori‑Zwanzig formalism. Neural networks parameterize the resulting non‑local free energy and mobility, trained on short MD trajectories generated with machine‑learning interatomic potentials. Demonstrations on an iron‑boron melt and hydrogen‑helium mixtures show the approach can predict pressure‑dependent stability, immiscibility boundaries, and large‑scale droplet dynamics beyond conventional atomistic simulations.
4DMulti is a physics‑guided learning framework that automates multicomponent identification from large‑scale four‑dimensional scanning transmission electron microscopy data. It leverages a 6‑million‑pattern diffraction database, a retrieval‑conditioned latent diffusion transformer (Sim2real) for realistic pattern generation, and a rotation‑invariant convolutional network for phase classification, achieving 98.82% accuracy on a five‑phase benchmark. The method introduces a diffraction‑inferred structural complexity metric and produces single‑nanometer‑resolution structural maps of complex material interfaces such as superconducting heterostructures, corroded alloys, and degraded solid‑state battery interfaces.
arXiv:2608. 14502v1 Announce Type: cross Abstract: Free energies govern solid-state phase stability, yet computational materials discovery still relies largely on ground-state energies because free energy calculations require ensemble averages.
arXiv:2602. 17176v4 Announce Type: replace-cross Abstract: Crystal structure prediction (CSP), which aims to predict the 3D atomic arrangement of a crystal from its composition, is central to materials discovery and mechanistic understanding.
Simulating microstructure evolution requires quantum-mechanical accuracy and mesoscopic reach in length and time scales, a combination that no current method achieves. Classical phase-field models pro...
arXiv:2609.14348v1 Announce Type: cross Abstract: Heterogeneous interfaces dictate the performance and degradation of functional materials, making it essential to link local structural variations wit...
arXiv:2607. 29095v1 Announce Type: new Abstract: Accurate prediction of lithium-ion battery state of health (SOH) is essential for reliable energy storage operation.