Enhancing the Efficiency and Flexibility of AutoMeKin: Integrating ORCA and Machine-Learning Potentials

Abstract

Automated reaction discovery is often limited by the cost of high-level refinement of large reaction networks. Here, we extend AutoMeKin, an automated reaction-discovery program, by presenting a complete integration of ORCA 6 into its high-level refinement workflow, providing access to a broad range of electronic-structure methods through a package freely available to academic users. We also integrate the UMA and MACE-OMOL-0 machine-learning interatomic potentials and Δ-ML energy corrections, enabling automated refinement of reaction networks using conventional electronic-structure methods, foundation ML potentials, or ML-corrected quantum-mechanical approaches within a unified framework. The workflow is assessed for five systems, monoethanolamine, three cyclobutadiene-derived radicals, and N-[bis(2H-triazol-4-yl)methylidene]hydroxylamine, spanning both in-distribution and challenging out-of-distribution chemistry. Comparisons with reference data show that UMA and MACE-OMOL-0 generally recover the relevant reaction mechanisms and provide energy profiles of comparable quality to DFT at substantially reduced cost. RRKM analyses further reveal that vibrational frequencies can significantly affect product population fractions, especially for loose transition states. These results show that the new implementation increases the accessibility and flexibility of AutoMeKin and further support the view that foundation ML potentials can serve as practical surrogates for DFT in automated reaction-network discovery.

Active Learning for Interatomic Potentials: A Review of Uncertainty-Driven Data Efficiency
Ilya V. Chepkasov, Alexander V. Shapeev, Alexander G. Kvashnin
arXiv16 Oct 2026PreprintCC BY 4.0Full textComputational physics Condensed matterarXiv
Spin-Resolved Reaction-Network Exploration in AutoMeKin with Minimum-Energy-Crossing-Point Localization and an ⟨S2⟩ Single-Reference Check
Joshua M. Sims
ChemRxiv23 Oct 2026PreprintCC BY-NC-NDFull textAtomic, molecular & optical physicsChemRxiv
Cu(In,Ga)Se₂ Solar Cells: Device Stability Based on Chemical Flexibility
Jean-François Guillemoles, Uwe Rau, Leeor Kronik +2
Advanced Materials22 Oct 2026Peer-reviewedCC BY-SA 4.0Full text121 citedMaterials & polymers Theoretical & computational chemistryHAL
Neural Network Fractional Quantum Hall States with Learnable Orbital Pair Matrix Factors: Balancing Physical Priors and Flexibility
Yubing Qian, Weiluo Ren, Ji Chen
arXiv20 Oct 2026PreprintarXiv licenceFull textCondensed matterarXiv
Combining heavy-tailed keystroke dynamics with eye tracking reveals temporal flexibility in human problem solving
Yavuz Dinc, Fabian Kieser, Peter Wulff +5
arXiv29 Oct 2026PreprintCC BY 4.0Full textGeneral physicsarXiv

Cite

BibTeX RIS

BibTeX

@article{rodriguezlopez2026enhancing,
  title = {{Enhancing the Efficiency and Flexibility of AutoMeKin: Integrating ORCA and Machine-Learning Potentials}},
  author = {Omar Rodríguez-López and Emilio Martínez-Núñez and Berta Fernández and Saulo A. Vázquez},
  journal = {Journal of Chemical Theory and Computation},
  year = {2026},
  month = oct,
  doi = {10.1021/acs.jctc.6c01565},
  url = {https://doi.org/10.1021/acs.jctc.6c01565},
  note = {CC BY 4.0},
}

RIS

TY  - JOUR
TI  - Enhancing the Efficiency and Flexibility of AutoMeKin: Integrating ORCA and Machine-Learning Potentials
AU  - Omar Rodríguez-López
AU  - Emilio Martínez-Núñez
AU  - Berta Fernández
AU  - Saulo A. Vázquez
PY  - 2026
DA  - 2026/10/06
JO  - Journal of Chemical Theory and Computation
DO  - 10.1021/acs.jctc.6c01565
UR  - https://doi.org/10.1021/acs.jctc.6c01565
AB  - Automated reaction discovery is often limited by the cost of high-level refinement of large reaction networks. Here, we extend AutoMeKin, an automated reaction-discovery program, by presenting a complete integration of ORCA 6 into its high-level refinement workflow, providing access to a broad range of electronic-structure methods through a package freely available to academic users. We also integrate the UMA and MACE-OMOL-0 machine-learning interatomic potentials and Δ-ML energy corrections, enabling automated refinement of reaction networks using conventional electronic-structure methods, foundation ML potentials, or ML-corrected quantum-mechanical approaches within a unified framework. The workflow is assessed for five systems, monoethanolamine, three cyclobutadiene-derived radicals, and N-[bis(2H-triazol-4-yl)methylidene]hydroxylamine, spanning both in-distribution and challenging out-of-distribution chemistry. Comparisons with reference data show that UMA and MACE-OMOL-0 generally recover the relevant reaction mechanisms and provide energy profiles of comparable quality to DFT at substantially reduced cost. RRKM analyses further reveal that vibrational frequencies can significantly affect product population fractions, especially for loose transition states. These results show that the new implementation increases the accessibility and flexibility of AutoMeKin and further support the view that foundation ML potentials can serve as practical surrogates for DFT in automated reaction-network discovery.
SN  - 1549-9618
SN  - 1549-9626
ER  -

Abstract

Related

Cite