期刊
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
卷 6, 期 7, 页码 2034-2039出版社
AMER CHEMICAL SOC
DOI: 10.1021/ct100214x
关键词
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资金
- U.S. National Science Foundation [CHE-CAREER 0844999]
- Gaussian Inc.
- University of Washington
- Division Of Chemistry
- Direct For Mathematical & Physical Scien [844999] Funding Source: National Science Foundation
An eigenspace update method is introduced in this article for molecular geometry optimization. This approach is used to obtain the nonredundant internal coordinate space and diagonalize the Hessian matrix. A select set of large molecules is tested and compared with the conventional method of direct diagonalization in redundant space. While all methods considered herein take on similar optimization pathways for most molecules tested, the eigenspace update algorithm becomes much more computationally efficient with increasing size of the molecular system. A factor of 3 speed-up in overall computational cost is observed in geometry optimization of the 25-alanine chain molecule. The contributing factors to the computational savings are the reduction to the much smaller nonredundant coordinate space and the O(N-2) scaling of the algorithm.
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