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Machine learning scheme for fast extraction of chemically interpretable interatomic potentials
We present a new method for a fast, unbiased and accurate representation of interatomic interactions. It is a combination of an artificial neural network and our new approach for pair potential reconstruction. The potential reconstruction method is simple and computationally cheap and gives rich inf...
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| Published in: | AIP advances 2016-08, Vol.6 (8), p.085318-085318-13 |
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| Main Authors: | , , |
| Format: | Article |
| Language: | English |
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| cited_by | cdi_FETCH-LOGICAL-c428t-56e60c05380ea9640ae317dbf4c71436782cf868827c8712085df8242069e6be3 |
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| cites | cdi_FETCH-LOGICAL-c428t-56e60c05380ea9640ae317dbf4c71436782cf868827c8712085df8242069e6be3 |
| container_end_page | 085318-13 |
| container_issue | 8 |
| container_start_page | 085318 |
| container_title | AIP advances |
| container_volume | 6 |
| creator | Dolgirev, Pavel E. Kruglov, Ivan A. Oganov, Artem R. |
| description | We present a new method for a fast, unbiased and accurate representation of interatomic interactions. It is a combination of an artificial neural network and our new approach for pair potential reconstruction. The potential reconstruction method is simple and computationally cheap and gives rich information about interactions in crystals. This method can be combined with structure prediction and molecular dynamics simulations, providing accuracy similar to ab initio methods, but at a small fraction of the cost. We present applications to real systems and discuss the insight provided by our method. |
| doi_str_mv | 10.1063/1.4961886 |
| format | article |
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| ispartof | AIP advances, 2016-08, Vol.6 (8), p.085318-085318-13 |
| issn | 2158-3226 2158-3226 |
| language | eng |
| recordid | cdi_crossref_primary_10_1063_1_4961886 |
| source | AIP Open Access Journals; Free Full-Text Journals in Chemistry |
| subjects | Artificial neural networks Computer simulation Crystal structure Machine learning Molecular dynamics Molecular structure Neural networks Organic chemistry Reconstruction Simulation |
| title | Machine learning scheme for fast extraction of chemically interpretable interatomic potentials |
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