arXiv AI By Bin Cao, Huichi Zhou, Runyu Yang, Jingsong Li, Shuchen Sun, Yan Song, Hanyu Gao, Zhongwei Yu, Tong-Yi Zhang, Jun Wang

A self-learning scientific agent for X-ray diffraction

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arXiv AI
Sep 2

AutoXRD: Autonomous LLM Agents and Comprehensive Evaluation for Powder Diffraction Analysis

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.

By Yuetong Wu, Maojun Sun
arXiv AI
Aug 19

Discovering physical mechanisms from experiment-simulation mismatches

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.

By Yue Li, Penghui Yang, Yushan Xiao, Zhonghan Zhang, Jianguo Huang, Yuhao Lu, Cuntai Guan, Bo An, Bijun Tang, Zheng Liu
arXiv AI
Sep 17

Hypothesis-Driven Autonomous Materials Synthesis with Multimodal LLM Agents

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.

By Izumi Takahara, Kazunori Nishio, Akira Aiba, Shigeru Kobayashi, Takao Nakajima, Taro Hitosugi, Teruyasu Mizoguchi
arXiv AI
Jun 12

Fantastic Scientific Agents and How to Build Them: AgentBuild for Rietveld Refinement

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.

By Woong Shin, Craig A. Bridges, Marshall T. McDonnell, Rafael Ferreira da Silva