arXiv:2601. 13508v4 Announce Type: replace-cross Abstract: Autonomous agents are beginning to transform scientific research from tool-assisted workflows toward self-sustaining discovery processes.
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 introduces ARCHE, an autonomous system that combines a general-purpose reasoning model, a domain-specialized computational chemistry model, and a structured tool registry to automate chemical mechanism discovery. ARCHE interprets scientific questions, generates and prioritizes mechanistic hypotheses, orchestrates computational workflows, and refines conclusions in a closed loop. The authors validate the system on three challenging scenarios, including reconstructing stereocontrolling transition states, proposing a radical pathway for an unpublished reaction, and identifying a descriptor governing selectivity in nickel-catalyzed cross‑coupling reactions.
arXiv:2512. 11935v2 Announce Type: replace Abstract: Agentic AI systems increasingly connect large language models (LLMs) to external scientific tools, yet whether and when tool access improves prediction accuracy remains uncharacterized.
arXiv:2506. 05616v3 Announce Type: replace Abstract: We aim at designing language agents with greater autonomy for crystal materials discovery.
arXiv:2602. 18008v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown promise in constructing mechanistic models from data.
El Agente Potente is an agentic system that integrates typed execution graphs and a coding mode to facilitate machine‑learning interatomic potential (MLIP) driven atomistic simulations. Typed execution graphs offer structured, provenance‑aware workflows where large language models handle planning and routing while deterministic Python code performs scientific computation and validation. The coding agent builds customized workflows for tasks needing procedural flexibility, invoking existing Potente functions for supported calculations. The system is demonstrated across materials discovery, energy‑landscape exploration, adsorption, and catalytic reaction workflows, with benchmarks on reproducibility and LLM token cost.
arXiv:2608. 15900v1 Announce Type: cross Abstract: Data-driven materials discovery interpolates more reliably than it extrapolates and seldom reaches new structure types.
arXiv:2608. 06694v1 Announce Type: new Abstract: Coarse-grained (CG) molecular dynamics extends polymer simulation beyond the scales accessible to all-atom (AA) methods, but bottom-up CG modeling is laborious.
arXiv:2606. 05050v1 Announce Type: cross Abstract: Theoretical heterogeneous catalysis promises rapid catalyst discovery, yet computational and machine-learning predictions often deviate from experiment and stay confined to narrow material families, for want of a faithful, condition-aware catalytic simulator.
arXiv:2603. 20986v2 Announce Type: replace Abstract: Phase-field modeling links thermodynamics and kinetics to microstructural evolution, but multiphysics frameworks such as MOOSE require expertise to construct inputs, manage campaigns, diagnose failures, and validate results.
arXiv:2606. 00138v1 Announce Type: new Abstract: Finite element analysis (FEA) is the most important numerical approach for solid mechanics.