Reaction Path Optimization and Sampling Methods and Their Applications for Rare Events

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The minimum energy pathway contains important information describing the transition between two states on a potential energy surface (PES). Chain-of-states methods were developed to efficiently calculate minimum energy pathways connecting two stable states. In the chain-of-states framework, a series of structures are generated and optimized to represent the minimum energy pathway connecting two states. However, multiple pathways may exist connecting two existing states and should be identified to obtain a full view of the transitions. Therefore, we developed an enhanced sampling method, named as the direct pathway dynamics sampling (DPDS) method, to facilitate exploration of a PES for multiple pathways connecting two stable states as well as addition minima and their associated transition pathways. In the DPDS method, molecular dynamics simulations are carried out on the targeting PES within a chain-of-states framework to directly sample the transition pathway space. The simulations of DPDS could be regulated by two parameters controlling distance among states along the pathway and smoothness of the pathway. One advantage of the chain-of-states framework is that no specific reaction coordinates are necessary to generate the reaction pathway, because such information is implicitly represented by the structures along the pathway. The chain-of-states setup in a DPDS method greatly enhances the sufficient sampling in high-energy space between two end states, such as transition states. By removing the constraint on the end states of the pathway, DPDS will also sample pathways connecting minima on a PES in addition to the end points of the starting pathway. This feature makes DPDS an ideal method to directly explore transition pathway space. Three examples demonstrate the efficiency of DPDS methods in sampling the high-energy area important for reactions on the PES.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamics Sampling in Transition Pathway Space

Zhou

Tao

2017

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“…In practice, the continuous pathway Γ is represented by a series of discrete structures, which can be generated using any chain-of-states methods. 29 The integration in eq 12 , therefore, can be calculated through summation. All the FEG-RBD simulations in this work were carried out using the CHARMM molecular simulation program and the CHARMM22 force field.…”

Section: Methodsmentioning

confidence: 99%

“…In practice, the continuous pathway Γ is represented by a series of discrete structures, which can be generated using any chain-of-states methods . The integration in eq , therefore, can be calculated through summation.…”

Section: Methodsmentioning

confidence: 99%

Computing the Free Energy along a Reaction Coordinate Using Rigid Body Dynamics

Tao

Sodt

Shao

et al. 2014

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The calculations of potential of mean force along complex chemical reactions or rare events pathways are of great interest because of their importance for many areas in chemistry, molecular biology, and material science. The major difficulty for free energy calculations comes from the great computational cost for adequate sampling of the system in high-energy regions, especially close to the reaction transition state. Here, we present a method, called FEG-RBD, in which the free energy gradients were obtained from rigid body dynamics simulations. Then the free energy gradients were integrated along a reference reaction pathway to calculate free energy profiles. In a given system, the reaction coordinates defining a subset of atoms (e.g., a solute, or the quantum mechanics (QM) region of a quantum mechanics/molecular mechanics simulation) are selected to form a rigid body during the simulation. The first-order derivatives (gradients) of the free energy with respect to the reaction coordinates are obtained through the integration of constraint forces within the rigid body. Each structure along the reference reaction path is separately subjected to such a rigid body simulation. The individual free energy gradients are integrated along the reference pathway to obtain the free energy profile. Test cases provided demonstrate both the strengths and weaknesses of the FEG-RBD method. The most significant benefit of this method comes from the fast convergence rate of the free energy gradient using rigid-body constraints instead of restraints. A correction to the free energy due to approximate relaxation of the rigid-body constraint is estimated and discussed. A comparison with umbrella sampling using a simple test case revealed the improved sampling efficiency of FEG-RBD by a factor of 4 on average. The enhanced efficiency makes this method effective for calculating the free energy of complex chemical reactions when the reaction coordinate can be unambiguously defined by a small subset of atoms within the system.

“…When this approach is performed in a QM/MM system, the interpolation is usually done only for the QM region, whereas the environment atoms in the MM region is generally approximated by the environment from one of the two local minima, and is replicated for all the images. This approximation of the MM environment may result in path discontinuities in early iterations, especially if overlapping atoms exist within the intermediate structures or if multiple MEPs exist between the end points. − …”

Section: Introductionmentioning

confidence: 99%

LICHEM 1.1: Recent Improvements and New Capabilities

Gökcan

Vázquez-Montelongo

Cisneros

2019

The QM/MM method has become a useful tool to investigate various properties of complex systems. We previously introduced the Layered Interacting Chemical Models (LICHEM) package to enable QM/MM simulations with advanced potentials by combining various (unmodified) QM and MM codes (JCC, 27, 1019). LICHEM provides several capabilities such as the ability to use polarizable force fields, such as AMOEBA, for the MM environment. Here, we describe an updated version of LICHEM (v1.1), which includes several new functionalities including a new method to account for long-range electrostatic effects in QM/mm (QM/MM-LREC), a new implementation for QM/MM with the Gaussian Electrostatic Model (GEM), and new capabilities for path optimizations using the quadratic string model (QSM) coupled with restrained MM environment optimization.