• Publications
  • Influence
Generalized neural-network representation of high-dimensional potential-energy surfaces.
TLDR
A new kind of neural-network representation of DFT potential-energy surfaces is introduced, which provides the energy and forces as a function of all atomic positions in systems of arbitrary size and is several orders of magnitude faster than DFT.
Atom-centered symmetry functions for constructing high-dimensional neural network potentials.
  • J. Behler
  • Computer Science
    The Journal of chemical physics
  • 16 February 2011
TLDR
Neural networks offer an unbiased and numerically very accurate approach to represent high-dimensional ab initio potential-energy surfaces and a transformation to symmetry functions is required to enable molecular dynamics simulations of large systems.
Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations.
  • J. Behler
  • Physics
    Physical chemistry chemical physics : PCCP
  • 4 October 2011
TLDR
In this Perspective, the current status of NN potentials is reviewed, and their advantages and limitations are discussed.
Perspective: Machine learning potentials for atomistic simulations.
  • J. Behler
  • Materials Science
    The Journal of chemical physics
  • 1 November 2016
TLDR
Recent advances in machine learning (ML) now offer an alternative approach for the representation of potential-energy surfaces by fitting large data sets from electronic structure calculations, which are reviewed along with a discussion of their current applicability and limitations.
Constructing high‐dimensional neural network potentials: A tutorial review
A lot of progress has been made in recent years in the development of atomistic potentials using machine learning (ML) techniques. In contrast to most conventional potentials, which are based on
Ab initio thermodynamics of liquid and solid water
TLDR
It is shown that nuclear-quantum effects contribute a crucial 0.2 meV/H2O to the stability of ice Ih, making it more stable than ice Ic, and the ab initio description leads to structural properties in excellent agreement with experiments and reliable estimates of the melting points of light and heavy water.
A Performance and Cost Assessment of Machine Learning Interatomic Potentials.
TLDR
A comprehensive evaluation of ML-IAPs based on four local environment descriptors --- atom-centered symmetry functions (ACSF), smooth overlap of atomic positions (SOAP), the Spectral Neighbor Analysis Potential (SNAP) bispectrum components, and moment tensors --- using a diverse data set generated using high-throughput density functional theory (DFT) calculations.
First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems.
  • J. Behler
  • Computer Science
    Angewandte Chemie
  • 9 October 2017
TLDR
The methodology of an important class of ML potentials that employs artificial neural networks is considered, which can accelerate computer simulations by several orders of magnitude, while preserving quantum mechanical accuracy.
Representing potential energy surfaces by high-dimensional neural network potentials.
  • J. Behler
  • Materials Science
    Journal of physics. Condensed matter : an…
  • 7 May 2014
TLDR
The basic methodology of high-dimensional NNPs will be presented with a special focus on the scope and the remaining limitations of this approach, e.g. for addressing problems in materials science, for investigating properties of interfaces, and for studying solvation processes.
Nucleation mechanism for the direct graphite-to-diamond phase transition.
TLDR
It is demonstrated that the large lattice distortions that accompany the formation of diamond nuclei inhibit the phase transition at low pressure, and direct it towards the hexagonal diamond phase at higher pressure.
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