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Global reaction route mapping (GRRM) program empowers users to do their treasure hunt of unknown molecules and reactions through automatic reaction path exploration from one starting point (equilibrium), driven by two core algorithms that collaborate with quantum chemistry calculation: artificial force induced reaction (AFIR) and anharmonic downward distortion following (ADDF). The power of ADDF and AFIR opens unknown chemistry of all four types of chemical reactions shown below , which leads users to explore possible output (molecules and reaction paths) comprehensively just by one single input of molecules.
(1) A → X (Isomerization)
(2) A → X+Y (Dissociation)
(3) A+B → X (Combining synthesis)
(4) A+B → X+Y (Exchanging synthesis)
|||S. Maeda, K. Ohno, K. Morokuma, "Systematic exploration of the mechanism of chemical reactions: the global reaction route mapping (GRRM) strategy using the ADDF and AFIR methods", Phys. Chem. Chem. Phys., 2013, 15, 3683-3701.|
Kinetic simulation and kinetics-based navigation using rate constant matrix contraction (RCMC) method became available from GRRM20 to enhance the automated reaction search function of GRRM, being applicable to complex reaction path networks. The kinetic simulation enables users to evaluate population of chemical species which exist beyond specified lifetime condition, while the kinetics-based navigation contributes to dramatic speedups of the reaction path search (SC-AFIR search of GRRM) by restricting the search areas to extract kinetically feasible paths from initial structures under given reaction temperatures and lifetimes .
|||Y. Sumiya, S. Maeda, "Rate Constant Matrix Contraction Method for Systematic Analysis of Reaction Path Networks", Chem. Lett, 49(5), 553-564, 2020.|
In GRRM23, the new function for reverse reaction kinetics analysis was implemented based on RCMC method (above-mentioned) for complex reaction path network analysis. This function predicts yields of target chemical products regarding all reactions starting from other chemical species on reaction path networks . By utilizing this function as a kinetics navigation tool, quantum chemistry-aided retrosynthetic analysis (QCaRA) is performed. So far, QCaRA, which takes structures of products as its sole input, has already demonstrated the ability to correctly identify reactants for various known reactions, including the synthesis of small natural products .
|||Y. Sumiya, Y. Harabuchi, Y. Nagata, S. Maeda, "Quantum Chemical Calculations to Trace Back Reaction Paths for the Prediction of Reactants", JACS Au, 2022, 2, 1181-1188.|
|||T. Mita, H. Takano, H. Hayashi, W. Kanna, Y. Harabuchi, K. N. Houk, S. Maeda, "Prediction of High-Yielding Single-Step or Cascade Pericyclic Reactions for the Synthesis of Complex Synthetic Targets", J. Am. Chem. Soc., 2022, 144, 22985-23000.|
GRRM23 provides functions to control structure optimization and exploration using externally developed modules. These modules can modify search order near local minima, change exploration routes from local minima, and apply external bias potentials to the system. These options are used in combination with rapidly exploring random tree algorithm  and graph neural network-based reaction path selection algorithm  in order to accelerate the reaction path exploration (SC-AFIR search of GRRM) for specific purposes. Moreover, they are also used for the development and application of virtual ligands for transition metal catalysts .
|||A. Nakao, Y. Harabuchi, S. Maeda, K. Tsuda, "Leveraging algorithmic search in quantum chemical reaction path finding", Phys. Chem. Chem. Phys., 2022, 24, 10305-10310.|
|||A. Nakao, Y. Harabuchi, S. Maeda, K. Tsuda, "Exploring the Quantum Chemical Energy Landscape with GNN-Guided Artificial Force", J. Chem. Theory Comput., 2023, 19, 713-717.|
|||W. Matsuoka, Y. Harabuchi, S. Maeda, "Virtual Ligand-Assisted Screening Strategy to Discover Enabling Ligands for Transition Metal Catalysis", ACS Catal., 2022, 12, 3752-3766.|
GRRM23 can be used to simulate various reaction systems, so far having already been applied in organic reaction, organometallic catalysis, cluster catalysis, radical reaction, photoreaction involving electronic excited states, crystal phase transition under periodic boundary conditions, enzyme catalysis reaction by QM/MM-ONIOM method, etc.
For example, in drug synthesis, GRRM program can be utilized for catalyst design targeting rate-determining reactions and for suppression of by-products by elucidating all reaction paths capable of synthesizing desired drug molecules.
For example, in combustion reaction, it is possible to meet the accuracy of reaction rates in CAE design of automotive and rocket engine because thousands to tens of thousands of elementary reactions can be obtained based on highly precise quantum chemistry calculations.
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|Number of stable structures (Reactants / Products / Intermediates)||Number of elementary reactions|
|*1||Not included in this product. Please prepare in advance.|
|*2||Not included in this product. Please prepare in advance as needed.|
|*3||A general interface to insert ab initio programs besides these is contained in GRRM23.|
|*4||Please refer to AFIR manual site for the latest GRRM23 info.|
For more information, please contact us.
We support users to set up and start GRRM23 (free of charge in standard).
As for the further technical information such as how to use and how to interpret results, please note in advance that users are encouraged to inquire through AFIR forum site (registration on AFIR site is required when purchasing GRRM23 license).
Please refer to AFIR manual site for the latest manual.
Attempt to empower computational chemistry calculations by the collaborating GRRM and AFIR with supercomputer FugakuNovemver 6th, 2023
Exhibiting at SC23 booth (Denvor, November, Booth No.1593)Novemver 1st, 2023
Released "GRRM23"!!May 19th, 2023