A data-driven approach to approximate the correlation functions in cluster variation method

Abhishek Kumar Thakur, Rajendra Prasad Gorrey, Vikas Jindal, Krishna Muralidharan

Research output: Contribution to journalArticlepeer-review


The cluster variation method is one of the thermodynamic models used to calculate phase diagrams considering short range order (SRO). This method predicts the SRO values through internal variables referred to as correlation functions (CFs), accurately up to the cluster chosen in modeling the system. Determination of these CFs at each thermodynamic state of the system requires solving a set of nonlinear equations using numerical methods. In this communication, a neural network model is proposed to predict the values of the CFs. This network is trained for the bcc phase under tetrahedron approximation for both ordering and phase separating systems. The results show that the network can predict the values of the CFs accurately and thereby Helmholtz energy and the phase diagram with significantly less computational burden than that of conventional methods used.

Original languageEnglish (US)
Article number015001
JournalModelling and Simulation in Materials Science and Engineering
Issue number1
StatePublished - Jan 2022


  • cluster variation method
  • correlation functions
  • neural network model
  • short range order
  • tetrahedron approximation

ASJC Scopus subject areas

  • Modeling and Simulation
  • Materials Science(all)
  • Condensed Matter Physics
  • Mechanics of Materials
  • Computer Science Applications


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