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The NEB (Nudged Elastic Band) method is widely used in computational chemistry, solid-state physics, and materials science to determine minimum energy paths (MEPs) between two known states of a system (typically, reactants and products). It is particularly useful for identifying the transition state (TS) and calculating energy barriers.
On the main toolbar, this is the icon that represents Nudged Elastic Band.
General idea
We have two known states: initial and final. The method inserts a set of intermediate “images” of the system (molecular structures or atomic configurations) that connect the two states.These images are linked to each other by artificial “springs” (hence the term elastic band).The optimization is carried out in such a way that the images relax onto the lowest-energy path possible, without collapsing into local minima (For more details, see the references at the end of the page).
Chair
Twisted boat
Figura 1: initial and final states
In EasyHybrid, the NEB can also be performed by accessing:
Main Menu > Simulate > Nudged Elastic Band (NEB)
An overview of the window for calculating NEB can be seen in Figure 1.
Figure 2: Overview of the Nudged Elastic Band (NEB) window. The window is divided into four sections: 1) Coordinates, 2) Basics, 3) Configuration of the geometry optimization parameters, and 4) Storage of the generated trajectories.”
Coordinates:
Reactants: Displayable object that refers to reactants.
Products: Displayable object that refers to reactants.
Fixed Terminal Images: Boolean, if True, keeps the initial and final images fixed, without optimization. These are usually the known states (reactant and product).
Basics (Optimization Control Parameters)
Number of Structures: It is the number of interpolated frames.
logFrequency: Frequency (in iterations) at which the optimization progress log is recorded.
maximumIterations: Maximum number of iterations allowed for the path optimization.
RMS Grad. Tolerance: Convergence criterion based on the root-mean-square (RMS) of the gradient: when the average gradient of the images falls below this value, the optimization is considered converged.
Spring Force Constant: Force constant of the artificial “springs” between images. Controls how strongly the images are kept spaced along the path.
Advances
Use Spline Redistribution: Boolean: if True, enables redistribution of images along the path using splines.
Force Spline Redistribution Check Per Iteration: Boolean: if True, checks the need for image redistribution at each iteration, even if the redistribution tolerance has not been reached.
splineRedistributionTolerance: Tolerance for redistributing images using splines. Indicates when images need to be redistributed to maintain uniform spacing.
freezeRMSGradientTolerance: Sets a specific RMS gradient for “frozen” images, ensuring they are not updated until conditions are satisfied.
Optimizer tolerance scaling: is a parameter that adjusts the strictness of the convergence criterion (typically the RMS gradient tolerance) during an optimization. By applying a scaling factor, certain regions or images can converge more loosely or more tightly depending on their importance.
Scaling factor > 1: Loosens the convergence criterion → images converge faster, but with less precision.
Scaling factor < 1: Tightens the convergence criterion → more precise, but takes longer to converge.
References
https://pubs.rsc.org/en/content/articlepdf/2011/cp/c0cp02828b?casa_token=wjKEHbAoCHUAAAAA:4YTocM2iYB4pCe5tVluXgYRiK6EOCsmWROBYGlM4S7OeVYTEb3Sq70FPmOxDzAV5R0jwGDuL2-_VSg
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