XDFT：从实验-模拟偏差中发现物理机制 Discovering physical mechanisms from experiment-simulation mismatches

精读摘要

科学发现常常始于观察与预测的分歧。随着计算与机器学习大规模扫描化学空间，实验-模拟的不一致被成规模暴露，但追查其物理机制仍靠专家人工。XDFT（eXplainable DFT）是一个自进化智能体，把这一过程变成可执行的搜索：候选机制被形式化为可执行假设，与实验对照裁决，并把整条轨迹蒸馏为先验供后续搜索使用，把「找机制」从专家手艺变成了自动化流程。 Scientific discovery often begins where observation and prediction disagree. As computation and ML survey chemical space at scale, experiment-simulation mismatches are exposed in bulk, but tracing them to physical mechanisms remains expert-led. XDFT is a self-evolving agent that turns this into an executable search, formalizing candidate mechanisms as executable hypotheses and distilling trajectories into priors for later searches.