1997
DOI: 10.1016/s0166-1280(97)00036-5
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Search for stationary points on multidimensional surfaces

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Cited by 10 publications
(10 citation statements)
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“…The algorithms for locating TSs can be generally divided into two classes, namely, (i) the chain-of-states methods and (ii) the surface-walking methods. The former class locates the TS by simultaneously optimizing a few connected structure images on the potential energy surface (PES) to identify the reaction path. The latter class requires only one image on the PES, and local information such as the gradient (force) or the second derivative (Hessian) of the PES is utilized to manipulate the image toward the TS. Despite the huge progress made in the field, the current theoretical methods for simulating reaction pathways are still not sufficiently efficient, and both computational power and human experiences are highly demanding. Specifically, the chain-of-states methods require a knowledge of the exact final state, and the success of the surface-walking method relies heavily on the input (TS-like) structures.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…The algorithms for locating TSs can be generally divided into two classes, namely, (i) the chain-of-states methods and (ii) the surface-walking methods. The former class locates the TS by simultaneously optimizing a few connected structure images on the potential energy surface (PES) to identify the reaction path. The latter class requires only one image on the PES, and local information such as the gradient (force) or the second derivative (Hessian) of the PES is utilized to manipulate the image toward the TS. Despite the huge progress made in the field, the current theoretical methods for simulating reaction pathways are still not sufficiently efficient, and both computational power and human experiences are highly demanding. Specifically, the chain-of-states methods require a knowledge of the exact final state, and the success of the surface-walking method relies heavily on the input (TS-like) structures.…”
Section: Introductionmentioning
confidence: 99%
“…Among all of the methods for TS searching, the Hessian-involved methods such as the P-RFO approach are perhaps the most efficient when the (analytic) Hessian is cheaply available. By modifying and following the eigenvalue of the Hessian (eigenvector following), these methods can maximize energy in one degree of freedom while minimize energy in all the others. To reduce the computational cost in calculating the Hessian, the Quasi-Newton-based methods have been utilized to update the Hessian, such as Powell-symmetric-Broyden (PSB) and Symmetric rank 1 (Murtagh–Sargent), and the hybrid approach developed by Bofill. In practice, a constraint on the step length is often implemented to deal with the overstepping problem .…”
Section: Introductionmentioning
confidence: 99%
“…Here, there are two commonly used possibilities to select the diagonal matrix elements: an uniform shift or individual shifts for each degree of freedom. The Restricted Step Rational Function Optimization (RS‐RFO), Augmented Hessian, stabilized Newton‐Raphson, and Trust Radius Newton methods are used the uniform shift computed in a way that the final step lies inside the “trust‐region,” that is,: ||Δboldqλ||R. …”
Section: Methodsmentioning
confidence: 99%
“…As a result, these methods are much less demanding in computational power. Belonging to this category are the methods, such as the partitioned rational function optimizer (P-RFO), the hybrid eigenvector following, , the dimer, and the bond-length constrained minimization methods. , …”
Section: Introductionmentioning
confidence: 99%
“…As a result, these methods are much less demanding in computational power. Belonging to this category are the methods, such as the partitioned rational function optimizer (P-RFO), [12][13][14][15] the hybrid eigenvector following, 16,17 the dimer, [18][19][20][21] and the bond-length constrained minimization methods. 22,23 Among all the methods in searching for TS, the Hessian involved methods, such as the P-RFO approach, are perhaps the most efficient when the (analytic) Hessian is cheaply available.…”
Section: Introductionmentioning
confidence: 99%