用可解释的量子计算自动发现实验与模拟不符背后的物理机制
Discovering physical mechanisms from experiment-simulation mismatches
- 将可能的物理机制转化为可执行假设，通过实验与模拟对比验证
- 在112个案例中解决105个金属-半导体预测偏差问题，单卡完成
- 自我进化学习，解决能力随经验提升，适合做机理探索的研究者
科学发现常始于观测与预测的差异。随着计算和机器学习对化学空间的覆盖，实验与模拟之间的不一致被大规模暴露，但追溯其物理机制仍依赖专家判断。本文提出可解释密度泛函理论（XDFT），一种自我演化的智能体，将这一过程变为可执行的搜索。XDFT将候选机制形式化为可执行假设，通过实验数据检验其后果，并将求解轨迹提炼为后续搜索的先验知识。该系统结合了从不匹配到机制发现的求解循环，以及通过求解过程不断优化求解器的学习循环。在112个经审核的案例中，标准计算预测为金属而实验显示为半导体，XDFT在单张GPU上成功解决了105例，并提供了有证据支持的机制。经过60例后，新机制在80%的保留案例中位列前三，相比初始专家先验的7%大幅提升。此外，对于七个专家设计的物理机制问题，XDFT也返回了带证据等级的机制。这些结果表明，实验-模拟不一致可作为科学智能体发现物理机制并学会寻找下一问题的可行起点。
原文摘要 · Abstract (English)
Scientific discovery often begins where observation and prediction disagree. As computation and machine learning survey chemical space, experiment-simulation mismatches are exposed at scale, while tracing them to physical mechanisms remains expert-led. Here we present eXplainable DFT (XDFT), a self-evolving agent that turns this process into an executable search. XDFT formalizes candidate mechanisms as executable hypotheses, adjudicates their consequences against experiment and distils trajectories into priors for later searches. This couples a solving loop from mismatch to mechanism with a learning loop through which solving changes the solver. Across 112 source-audited cases in which standard calculations predict a metal whereas experiments find a semiconductor, XDFT resolved 105 with evidence-supported mechanisms within a single-GPU envelope. After 60 cases, the resolving mechanism ranked among the first three hypotheses for 80% of held-out cases, up from 7% under the initial expert prior. XDFT also returned evidence-graded mechanisms for seven expert-curated questions about physical mechanisms. These results establish experiment-simulation mismatches as tractable starting points for scientific agents that discover physical mechanisms while learning how to find the next.
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