Dataset.

Neural-network force field backed nested sampling: Study of the silicon p-T phase diagram. Supplementary Material

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/343494
Digital.CSIC. Repositorio Institucional del CSIC
  • Unglert, Nico
  • Carrete, Jesús
  • Pártay, Livia B.
  • Madsen, Georg K.H.
Analysis of phases visited during the nested sampling algorithm., Peer reviewed
 
DOI: http://hdl.handle.net/10261/343494
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/343494

HANDLE: http://hdl.handle.net/10261/343494
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/343494
 
Ver en: http://hdl.handle.net/10261/343494
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/343494

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/343491
Artículo científico (article). 2023

NEURAL-NETWORK FORCE FIELD BACKED NESTED SAMPLING: STUDY OF THE SILICON P-T PHASE DIAGRAM

Digital.CSIC. Repositorio Institucional del CSIC
  • Unglert, Nico
  • Carrete, Jesús
  • Pártay, Livia B.
  • Madsen, Georg K.H.
Nested sampling is a promising method for calculating phase diagrams of materials. However, if accuracy at the level of ab initio calculations is required, the computational cost limits its applicability. In the present work, we report on the efficient use of a neural-network force field in conjunction with the nested-sampling algorithm. We train our force fields on a recently reported database of silicon structures evaluated at the level of density functional theory and demonstrate our approach on the low-pressure region of the silicon pressure-temperature phase diagram between 0 and 16GPa. The simulated phase diagram shows good agreement with experimental results, closely reproducing the melting line. Furthermore, all of the experimentally stable structures within the investigated pressure range are also observed in our simulations. We point out the importance of the choice of exchange-correlation functional for the training data and show how the r2SCAN meta-generalized gradient approximation plays a pivotal role in achieving accurate thermodynamic behavior. We furthermore perform a detailed analysis of the potential energy surface exploration and highlight the critical role of a diverse and representative training data set., L.B.P. acknowledges support from EPSRC through the individual Early Career Fellowship (EP/T000163/1)., Peer reviewed




Zaguán. Repositorio Digital de la Universidad de Zaragoza
oai:zaguan.unizar.es:129959
Artículo científico (article). 2023

NEURAL-NETWORK FORCE FIELD BACKED NESTED SAMPLING: STUDY OF THE SILICON P-T PHASE DIAGRAM

Zaguán. Repositorio Digital de la Universidad de Zaragoza
  • Unglert, Nico
  • Carrete, Jesús
  • Pártay, Livia B.
  • Madsen, Georg K. H.
Nested sampling is a promising method for calculating phase diagrams of materials. However, if accuracy at the level of ab initio calculations is required, the computational cost limits its applicability. In the present work, we report on the efficient use of a neural-network force field in conjunction with the nested-sampling algorithm. We train our force fields on a recently reported database of silicon structures evaluated at the level of density functional theory and demonstrate our approach on the low-pressure region of the silicon pressure-temperature phase diagram between 0 and 16 GPa. The simulated phase diagram shows good agreement with experimental results, closely reproducing the melting line. Furthermore, all of the experimentally stable structures within the investigated pressure range are also observed in our simulations. We point out the importance of the choice of exchange-correlation functional for the training data and show how the r2SCAN meta-generalized gradient approximation plays a pivotal role in achieving accurate thermodynamic behavior. We furthermore perform a detailed analysis of the potential energy surface exploration and highlight the critical role of a diverse and representative training data set.



Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/343494
Dataset. 2023

NEURAL-NETWORK FORCE FIELD BACKED NESTED SAMPLING: STUDY OF THE SILICON P-T PHASE DIAGRAM. SUPPLEMENTARY MATERIAL

Digital.CSIC. Repositorio Institucional del CSIC
  • Unglert, Nico
  • Carrete, Jesús
  • Pártay, Livia B.
  • Madsen, Georg K.H.
Analysis of phases visited during the nested sampling algorithm., Peer reviewed




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