Discovering physical mechanisms from experiment-simulation mismatches
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 st...
| Authors | Yue Li, Penghui Yang, Yushan Xiao et al. |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 29 April 2026 |
| Last updated at source | 18 August 2026 |
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