A machine learning based deep potential for seeking the low-lying candidates of Al clusters
A Machine-Learning based Deep Potential (DP) model for Al clusters is developed through training with an extended database including ab initio data of both bulk and several clusters in only 6 CPU/h. This DP model has good performance in accurately predicting the low-lying candidates of Al clusters i...
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| Veröffentlicht in: | The Journal of chemical physics 2020-03, Vol.152 (11), p.114105-114105, Article 114105 |
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| container_end_page | 114105 |
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| container_issue | 11 |
| container_start_page | 114105 |
| container_title | The Journal of chemical physics |
| container_volume | 152 |
| creator | Tuo, P. Ye, X. B. Pan, B. C. |
| description | A Machine-Learning based Deep Potential (DP) model for Al clusters is developed through training with an extended database including ab initio data of both bulk and several clusters in only 6 CPU/h. This DP model has good performance in accurately predicting the low-lying candidates of Al clusters in a broad size range. Based on our developed DP model, the low-lying structures of 101 different sized Al clusters are extensively searched, among which the lowest-energy candidates of 69 sized clusters are updated. Our calculations demonstrate that machine-learning is indeed powerful in generating potentials to describe the interaction of atoms in complex materials. |
| doi_str_mv | 10.1063/5.0001491 |
| format | Article |
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B.</creatorcontrib><creatorcontrib>Pan, B. C.</creatorcontrib><title>A machine learning based deep potential for seeking the low-lying candidates of Al clusters</title><title>The Journal of chemical physics</title><addtitle>J CHEM PHYS</addtitle><addtitle>J Chem Phys</addtitle><description>A Machine-Learning based Deep Potential (DP) model for Al clusters is developed through training with an extended database including ab initio data of both bulk and several clusters in only 6 CPU/h. This DP model has good performance in accurately predicting the low-lying candidates of Al clusters in a broad size range. Based on our developed DP model, the low-lying structures of 101 different sized Al clusters are extensively searched, among which the lowest-energy candidates of 69 sized clusters are updated. Our calculations demonstrate that machine-learning is indeed powerful in generating potentials to describe the interaction of atoms in complex materials.</description><subject>Chemistry</subject><subject>Chemistry, Physical</subject><subject>Clusters</subject><subject>Machine learning</subject><subject>Performance prediction</subject><subject>Physical Sciences</subject><subject>Physics</subject><subject>Physics, Atomic, Molecular & Chemical</subject><subject>Science & Technology</subject><issn>0021-9606</issn><issn>1089-7690</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>AOWDO</sourceid><recordid>eNqN0E1r3DAQBmBRWppt2kP_QBH00g-cjixLlo_L0o9AIJf01IOR5VHj1Cu5kpyQf18Zb7fQQCkIhMQzLzNDyEsGZwwk_yDOAIBVDXtENgxUU9SygcdkA1CyopEgT8izGG8WVJfVU3LCS9Y0FRcb8m1L99pcDw7piDq4wX2nnY7Y0x5xopNP6NKgR2p9oBHxxwLSddb-rhjvl5fRrh96nTBSb-l2pGacY8IQn5MnVo8RXxzuU_L108er3Zfi4vLz-W57URiueCo66BkIUTHWW2MYMN3JUoJQssJ8uAHFG2utkVagYqI0nTa80bWpDBO85KfkzZo7Bf9zxpja_RANjqN26OfYllwxVak8cKav_6I3fg4ud5dVXYuqqWFRb1dlgo8xoG2nMOx1uG8ZtMvGW9EeNp7tq0Pi3O2xP8rfK87g_QrusPM2mgGdwSPLMaJkXAKHJTFr9f96NySdBu92fnYpl75bS3PV-n-su_XhT8vt1Nt_4Ycz_gLYy7YQ</recordid><startdate>20200321</startdate><enddate>20200321</enddate><creator>Tuo, P.</creator><creator>Ye, X. 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C.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A machine learning based deep potential for seeking the low-lying candidates of Al clusters</atitle><jtitle>The Journal of chemical physics</jtitle><stitle>J CHEM PHYS</stitle><addtitle>J Chem Phys</addtitle><date>2020-03-21</date><risdate>2020</risdate><volume>152</volume><issue>11</issue><spage>114105</spage><epage>114105</epage><pages>114105-114105</pages><artnum>114105</artnum><issn>0021-9606</issn><eissn>1089-7690</eissn><coden>JCPSA6</coden><abstract>A Machine-Learning based Deep Potential (DP) model for Al clusters is developed through training with an extended database including ab initio data of both bulk and several clusters in only 6 CPU/h. This DP model has good performance in accurately predicting the low-lying candidates of Al clusters in a broad size range. 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| source | AIP Journals Complete; Web of Science - Science Citation Index Expanded - 2020<img src="https://exlibris-pub.s3.amazonaws.com/fromwos-v2.jpg" />; Alma/SFX Local Collection |
| subjects | Chemistry Chemistry, Physical Clusters Machine learning Performance prediction Physical Sciences Physics Physics, Atomic, Molecular & Chemical Science & Technology |
| title | A machine learning based deep potential for seeking the low-lying candidates of Al clusters |
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