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Finding minimum energy paths using Gaussian process regression ∗
| Content Provider | Semantic Scholar |
|---|---|
| Author | Koistinen, Olli-Pekka |
| Copyright Year | 2017 |
| Abstract | In computational chemistry, systems of atoms are often characterized by a potential energy surface defined as a function of the atom coordinates. When determining the mechanism and rate of transitions between different configurations, it is useful to find a minimum energy path connecting the energy minimum points corresponding to the initial and final state. Such paths are frequently calculated using the nudged elastic band method, where an initial path is iteratively shifted to the nearest minimum energy path. The computational effort can be large, since the method typically requires hundreds of expensive evaluations of energy and its gradient. Here, we show how the number of such evaluations can be reduced by an order of magnitude using a Gaussian process regression approach where an approximate energy surface is generated and refined in each iteration. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://bayesopt.github.io/papers/2017/19.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
