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Adaptive Subsystem Density Functional Theory.

Xuecheng ShaoAndres Cifuentes LopezMd Rajib Khan MusaMohammad Reza NouriMichele Pavanello
Published in: Journal of chemical theory and computation (2022)
Subsystem density functional theory (DFT) is emerging as a powerful electronic structure method for large-scale simulations of molecular condensed phases and interfaces. Key to its computational efficiency is the use of approximate nonadditive noninteracting kinetic energy functionals. Unfortunately, currently available nonadditive functionals lead to inaccurate results when the subsystems interact strongly such as when they engage in chemical reactions. This work disrupts the status quo by devising a workflow that extends subsystem DFT's applicability also to strongly interacting subsystems. This is achieved by implementing a fully automated adaptive definition of subsystems which is realized during geometry optimizations or ab initio molecular dynamics simulations. The new method prescribes subsystem merging and splitting events redistributing the resources (both for work and data) in an efficient way making use of modern parallelization strategies and object-oriented programming. We showcase the method with examples probing from moderate-to-strong inter-subsystem interactions, opening the door to using subsystem DFT for modeling chemical reactions in molecular condensed phases with a black box computational tool.
Keyphrases
  • density functional theory
  • molecular dynamics
  • molecular dynamics simulations
  • molecular docking
  • electronic health record
  • machine learning
  • transcription factor
  • deep learning
  • high intensity
  • big data