Unbiased Reaction Path Search (URPS) approach 

Predicting the mechanism of simple two-component reactions is relatively straightforward using a biased approach based on chemical knowledge and intuition. However, reactions with three or more components are significantly more complex and require an exhaustive and unbiased exploration of potential mechanisms. To address this challenge, the Unbiased Reaction Path Search (URPS) approach was developed. URPS efficiently explores multiple reaction pathways at a reasonable computational cost and uses machine learning to predict all possible low-energy reaction paths.

Reaction Path Refinement Algorithm (RPRA)

The RPRA tool investigates complex reaction networks to gain a deeper understanding of the most favorable low-energy pathways, which are often crucial for understanding the overall reaction mechanism. Once the low-energy reaction paths are identified, RPRA stores them in a structured format, making them available for detailed visualization and further analysis. This capability allows researchers to explore, compare, and optimize reaction mechanisms more effectively.

PyQMMM

PyQMMM is a Python-based interface that combines modern polarizable and non-polarizable force fields with subtractive QM/MM methods. Our goal is to use the AMOEBA09 polarizable force field with subtractive QM/MM to determine the chemical processes on 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.