Artificial neural network correction for density-functional tight-binding molecular dynamics simulations
In this paper, the authors developed a Behler–Parrinello-type neural network (NN) to improve the density-functional tight-binding (DFTB) energy and force prediction. The Δ-machine learning approach was adopted and the NN was designed to predict the energy differences between the density functional theory (DFT) quantum chemical potential and DFTB for a given molecular structure. Most notably, the DFTB-NN method is capable of improving the energetics of intramolecular hydrogen bonds and torsional potentials without modifying the framework of DFTB itself. This improvement enables considerably larger simulations of complex chemical systems that currently could not easily been accomplished using DFT or higher level ab initio quantum chemistry methods alone.
- Research Organization:
- Energy Frontier Research Centers (EFRC) (United States). Fluid Interface Reactions, Structures and Transport Center (FIRST); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities Division
- Grant/Contract Number:
- AC05-00OR22725
- OSTI ID:
- 1543210
- Journal Information:
- MRS Communications, Vol. 9, Issue 3; ISSN 2159-6859
- Publisher:
- Materials Research Society - Cambridge University PressCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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