El Agente Quntur: Agentic Active-Space Selection for CASSCF
Abstract
Multireference calculations often require substantial human expertise and time to choose and refine an active space for the electronic states or chemical process of interest. This involves selecting which orbitals to include, verifying that the calculation describes the intended excited state, and deciding whether the results have converged. Here, we ask whether scientific agentic systems (harnesses) can make these decisions reliably. We use the El Agente Quntur multi-agent harness, with GPT-5.6 Sol and GPT-5.6 Terra as the underlying LLM components, to explore 32 diverse singlet excitations at fixed molecular geometries. The agents planned and executed calculations, choosing and refining active spaces under prompts that specified equal-weight two-singlet state-averaged CASSCF followed by strongly contracted NEVPT2 with def2-TZVPD. We evaluate excitation energies against published CASPT2 reference values at matching geometries and compare performance with six deterministic Active Space Finder protocols. Within the El Agente Quntur system, agents detect missing active orbitals, compare candidate excited states, and revise state assignments as calculations progress. Agentic execution reduced mean absolute error by 48\% relative to the state-of-the-art Active Space Finder protocol. We note that computational traces vary substantially across runs in both active space selection strategies and cost, even for the same molecule. Our results underscore the advantage of agentic active space selection over fixed workflows, yet leave room to improve efficiency through runtime-aware decision-making, reduced inter-agent communication overhead and redundancy, more systematic checks of state identity, and clearer stopping criteria.
Related
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BibTeX
@misc{perezsanchez2026agente,
title = {{El Agente Quntur: Agentic Active-Space Selection for CASSCF}},
author = {Juan B. Pérez-Sánchez and Viviana Roman-Ventura and Olalla Nieto-Faza and Aiwei Yin and Alán Aspuru-Guzik and Varinia Bernales},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.38145},
eprint = {2610.38145},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.38145},
}
RIS
TY - UNPB
TI - El Agente Quntur: Agentic Active-Space Selection for CASSCF
AU - Juan B. Pérez-Sánchez
AU - Viviana Roman-Ventura
AU - Olalla Nieto-Faza
AU - Aiwei Yin
AU - Alán Aspuru-Guzik
AU - Varinia Bernales
PY - 2026
DA - 2026/10/28
PB - arXiv
DO - 10.48550/arxiv.2610.38145
UR - https://arxiv.org/abs/2610.38145
AB - Multireference calculations often require substantial human expertise and time to choose and refine an active space for the electronic states or chemical process of interest. This involves selecting which orbitals to include, verifying that the calculation describes the intended excited state, and deciding whether the results have converged. Here, we ask whether scientific agentic systems (harnesses) can make these decisions reliably. We use the El Agente Quntur multi-agent harness, with GPT-5.6 Sol and GPT-5.6 Terra as the underlying LLM components, to explore 32 diverse singlet excitations at fixed molecular geometries. The agents planned and executed calculations, choosing and refining active spaces under prompts that specified equal-weight two-singlet state-averaged CASSCF followed by strongly contracted NEVPT2 with def2-TZVPD. We evaluate excitation energies against published CASPT2 reference values at matching geometries and compare performance with six deterministic Active Space Finder protocols. Within the El Agente Quntur system, agents detect missing active orbitals, compare candidate excited states, and revise state assignments as calculations progress. Agentic execution reduced mean absolute error by 48\% relative to the state-of-the-art Active Space Finder protocol. We note that computational traces vary substantially across runs in both active space selection strategies and cost, even for the same molecule. Our results underscore the advantage of agentic active space selection over fixed workflows, yet leave room to improve efficiency through runtime-aware decision-making, reduced inter-agent communication overhead and redundancy, more systematic checks of state identity, and clearer stopping criteria.
ER -