A self-learning scientific agent for X-ray diffraction
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AutoXRD is an autonomous large language model (LLM) agent framework designed to automate powder X-ray diffraction (XRD) analysis by structuring the process as stepwise refinement, grounding actions in observed evidence, and applying deterministic crystallographic and physical checks. The authors introduce XRDBench, comprising two tracks: XRDBench-QA with 100 diagnostic tasks focused on scientific reasoning, and XRDBench-E2E with 34 executable workflows that test full analysis capabilities, including file inspection, software execution, iterative refinement, evidence preservation, and reporting. Evaluation of ten recent LLMs on 1,340 model–task runs shows average scores of 57.8, with GPT‑5.6 Sol achieving the highest overall score of 81.1; the study also identifies key failure modes such as coupled‑parameter control and quantitative reasoning, highlighting areas for future improvement.
The paper introduces eXplainable DFT (XDFT), a self‑evolving computational agent that transforms experiment‑simulation mismatches into executable searches for physical mechanisms. XDFT formalizes candidate mechanisms as hypotheses, tests them against experimental data, and refines its search strategy through a learning loop. In a benchmark of 112 cases where standard calculations predicted a metal but experiments found a semiconductor, XDFT resolved 105 cases with evidence‑supported mechanisms, and its top‑ranked hypotheses improved dramatically over initial expert priors.
arXiv:2608. 11224v1 Announce Type: new Abstract: Materials research advances through accumulated experience - scripts that work, protocols that are trusted, warnings attached to failed calculations or experiments, and judgement that links a new question to an old result.
SynAgent is a framework that uses large language model agents to run autonomous experiments while building an explicit, revisable understanding of the synthesis process. Unlike traditional black‑box optimizers, SynAgent generates analysis skills on the fly and reasons multimodally over data such as X‑ray diffraction patterns and electron micrographs. In an 18‑experiment campaign on LiCoO₂ thin‑film deposition, SynAgent produced highly crystalline films and uncovered a sharp temperature threshold and optimal growth window (650–690 °C) for crystallization.
arXiv:2606. 12834v1 Announce Type: new Abstract: As scientific workflows shift from deterministic executables to LLM-based agents, the development practices on offer, such as fine-tuning, reinforcement learning, and prompt-and-go, bury the scientist's judgment.
Self-driving laboratories can explore synthesis conditions autonomously, but their decision-making layer is typically a black-box optimizer, and the output is a set of optimized samples, with the meas...