@Article{Dong2026,
author={Dong, Lechen
and Liu, Fang},
title={Early Detection and Recovery of SCF Convergence Failures in Automated Quantum Chemistry Workflows via Time-Series Learning},
journal={Journal of Chemical Theory and Computation},
year={2026},
month={Aug},
day={25},
abstract={Self-consistent field (SCF) convergence failures represent a major bottleneck in high-throughput workflows, particularly for open-shell systems and material discovery. To address this challenge, we introduce an autonomous pipeline that treats the electronic structure descriptors from early spin-unrestricted SCF iterations as a time-series problem. Using statistical descriptors extracted from only ten iterations, a lightweight gradient-boosted classifier accurately predicts convergence failures. The model achieves 94.8{\%} accuracy on 55,000 doublet anionic QM9 test molecules after being trained on only 10,000 examples. We then couple this early warning predictor with restart-based level-shifting interventions derived from Bayesian optimization of the $\alpha$- and $\beta$-shifting parameters. Across six diverse SCF test sets, our pipeline successfully rescued 53.9{\%} of inherently difficult-to-converge molecules while maintaining a 96.3{\%} convergence rate for easy-to-converge cases, reducing the total number of iterations across the 1,200 calculations by 248,355 steps. Overall, this proof-of-concept demonstrates that treating early SCF iterations as a time-series problem provides a data-efficient active-monitoring layer, which can be paired with various remedies to fine-tune convergence parameters and enable more autonomous, large-scale electronic structure workflows.},
issn={1549-9618},
doi={10.1021/acs.jctc.6c00928},
url={https://doi.org/10.1021/acs.jctc.6c00928}
}