PyQMMM

PyQMMM is a python-based interface that combines modern polarizable or non-polarizable forcefields with the subtractive QM/MM methods. Our goal is to use the AMOEBA09 polarizable force field with the subtractive QM/MM to determine the chemical processes on the interstellar ices.

P. V. G. M. Rathnayake, W. M. C. Sameera,* N. Watanabe, ChemRxiv, 2022. DOI: 10.26434/chemrxiv-2022-2c8mz PyQMMM is free for academic research

W. M. C. Sameera,* A. P. Jayaweera, A. Ishibashi, H. Hidaka, Y. Oba, N. Watanabe.  Faraday Discuss., 2023, 245, 508-518.


SICTWO

The ONIOM(QM:MM) implementation in SICTWO supports the AMOEBA polarizable model, Liam Dang's polarizable model, AMBER, CHARMM, MM2, MM3, OPLS-AA, MMFF, etc.

W. M. C. Sameera* et al. J. Phy. Chem. A, 2021, 125, 1, 387–393.
W. M. C. Sameera* et al. J. Phy. Chem. C, 2017, 121, 15223-15232.
W. M. C. Sameera,* F. Maseras, J. Chem. Info. Model. 2018, 58, 1828-1835.


MC-AFIR

The multi-component artificial force induced reaction (MC-AFIR) method can be used for the comprehensive and systematic determination of reaction mechanisms.

W. M. C. Sameera, Y. Sumiya, B. B. Skjelstad, S. Maeda, Automated Mechanism Discovery, Comprehensive Computational Chemistry, 2024, 4, 454-484.
W. M. C. Sameera, S. Maeda, K. Morokuma, Acc. Chem. Res. 2016, 49, 763-773.
W. M. C. Sameera, A. K. Sharma, S. Maeda, K. Morokuma, Chem. Rec., 16, 2016, 2349-2363.