Qcforever2: Advanced Automation of Quantum Chemistry Computations

doi:10.1002/jcc.70017

. 2025 Jan 30;46(3):e70017.

doi: 10.1002/jcc.70017.

Qcforever2: Advanced Automation of Quantum Chemistry Computations

Masato Sumita¹, Kei Terayama^{1

2

3}, Shoichi Ishida², Kensuke Suga^{4

5}, Shohei Saito⁵, Koji Tsuda^{1

6

7}

Affiliations

¹ RIKEN Center for Advanced Intelligence Project, Tokyo, Japan.
² Graduate School of Medical Life Science, Yokohama City University, Tsurumi-ku, Japan.
³ MDX Research Center for Element Strategy, Tokyo Institute of Technology, Yokohama, Kanagawa, Japan.
⁴ Department of Chemistry, Graduate School of Science, Kyoto University, Kyoto, Japan.
⁵ Department of Chemistry, Graduate School of Science, Osaka University, Osaka, Japan.
⁶ Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan.
⁷ Research and Services Division of Materials Data and Integrated System, National Institute for Materials Science, Tsukuba, Japan.

PMID: 39865308
DOI: 10.1002/jcc.70017

Qcforever2: Advanced Automation of Quantum Chemistry Computations

Masato Sumita et al. J Comput Chem. 2025.

. 2025 Jan 30;46(3):e70017.

doi: 10.1002/jcc.70017.

Authors

Masato Sumita¹, Kei Terayama^{1

2

3}, Shoichi Ishida², Kensuke Suga^{4

5}, Shohei Saito⁵, Koji Tsuda^{1

6

7}

Affiliations

¹ RIKEN Center for Advanced Intelligence Project, Tokyo, Japan.
² Graduate School of Medical Life Science, Yokohama City University, Tsurumi-ku, Japan.
³ MDX Research Center for Element Strategy, Tokyo Institute of Technology, Yokohama, Kanagawa, Japan.
⁴ Department of Chemistry, Graduate School of Science, Kyoto University, Kyoto, Japan.
⁵ Department of Chemistry, Graduate School of Science, Osaka University, Osaka, Japan.
⁶ Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan.
⁷ Research and Services Division of Materials Data and Integrated System, National Institute for Materials Science, Tsukuba, Japan.

PMID: 39865308
DOI: 10.1002/jcc.70017

Abstract

QCforever is a wrapper designed to automatically and simultaneously calculate various physical quantities using quantum chemical (QC) calculation software for blackbox optimization in chemical space. We have updated it to QCforever2 to search the conformation and optimize density functional parameters for a more accurate and reliable evaluation of an input molecule. In blackbox optimization, QCforever2 can work as compactly arranged surrogate models for costly chemical experiments. QCforever2 is the future of QC calculations and would be a good companion for chemical laboratories, providing more reliable search and exploitation in the chemical space.

Keywords: Bayesian optimization; conformation search; range separated parameter; spin multiplicity.

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References

1. K. Terayama, M. Sumita, R. Tamura, and K. Tsuda, “Black‐Box Optimization for Automated Discovery,” Accounts of Chemical Research 54 (2021): 1334.
1. R. Pollice, G. G. Dos Passos, M. Aldeghi, et al., “Data‐Driven Strategies for Accelerated Materials Design,” Accounts of Chemical Research 54, no. 4 (2021): 849–860.
1. F. Ren, L. Ward, T. Williams, et al., “Accelerated Discovery of Metallic Glasses Through Iteration of Machine Learning and High‐Throughput Experiments,” Science Advances 4, no. 4 (2018): eaaq1566.
1. K. Homma, Y. Liu, M. Sumita, et al., “Optimization of a Heterogeneous Ternary Li3PO4–Li3BO3–Li2SO4 Mixture for Li‐Ion Conductivity by Machine Learning,” Journal of Physical Chemistry C 124 (2020): 12865–12870.
1. M. Sumita, R. Tamura, K. Homma, and K. Tsuda, “Li‐Ion Conductive Li3PO4–Li3BO3–Li2SO4Mixture:Prevision Through Density Functional Molecular Dynamics and Machine Learning,” Bulletin of the Chemical Society of Japan 92 (2019): 1100–1106.

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[1] K. Terayama, M. Sumita, R. Tamura, and K. Tsuda, “Black‐Box Optimization for Automated Discovery,” Accounts of Chemical Research 54 (2021): 1334.

[2] K. Terayama, M. Sumita, R. Tamura, and K. Tsuda, “Black‐Box Optimization for Automated Discovery,” Accounts of Chemical Research 54 (2021): 1334.

[3] R. Pollice, G. G. Dos Passos, M. Aldeghi, et al., “Data‐Driven Strategies for Accelerated Materials Design,” Accounts of Chemical Research 54, no. 4 (2021): 849–860.

[4] R. Pollice, G. G. Dos Passos, M. Aldeghi, et al., “Data‐Driven Strategies for Accelerated Materials Design,” Accounts of Chemical Research 54, no. 4 (2021): 849–860.

[5] F. Ren, L. Ward, T. Williams, et al., “Accelerated Discovery of Metallic Glasses Through Iteration of Machine Learning and High‐Throughput Experiments,” Science Advances 4, no. 4 (2018): eaaq1566.

[6] F. Ren, L. Ward, T. Williams, et al., “Accelerated Discovery of Metallic Glasses Through Iteration of Machine Learning and High‐Throughput Experiments,” Science Advances 4, no. 4 (2018): eaaq1566.

[7] K. Homma, Y. Liu, M. Sumita, et al., “Optimization of a Heterogeneous Ternary Li3PO4–Li3BO3–Li2SO4 Mixture for Li‐Ion Conductivity by Machine Learning,” Journal of Physical Chemistry C 124 (2020): 12865–12870.

[8] K. Homma, Y. Liu, M. Sumita, et al., “Optimization of a Heterogeneous Ternary Li3PO4–Li3BO3–Li2SO4 Mixture for Li‐Ion Conductivity by Machine Learning,” Journal of Physical Chemistry C 124 (2020): 12865–12870.

[9] M. Sumita, R. Tamura, K. Homma, and K. Tsuda, “Li‐Ion Conductive Li3PO4–Li3BO3–Li2SO4Mixture:Prevision Through Density Functional Molecular Dynamics and Machine Learning,” Bulletin of the Chemical Society of Japan 92 (2019): 1100–1106.

[10] M. Sumita, R. Tamura, K. Homma, and K. Tsuda, “Li‐Ion Conductive Li3PO4–Li3BO3–Li2SO4Mixture:Prevision Through Density Functional Molecular Dynamics and Machine Learning,” Bulletin of the Chemical Society of Japan 92 (2019): 1100–1106.

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Qcforever2: Advanced Automation of Quantum Chemistry Computations

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Qcforever2: Advanced Automation of Quantum Chemistry Computations

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