Ab Initio Molecular Dynamics: Basic Theory and Advanced Methods

By D. Marx, J. Hutter. 2010

505 citations recorded by the source corpus.

Subfields: Physical and Theoretical Chemistry, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/13ab8b27c24faa61ee28600648b24ee455796eef

Citations in the corpus

• Computational understanding of Li-ion batteries (2016, unknown): “For a general introduction to the subject we refer the reader to standard text books.(86,87) AIMD simulations can most readily address the limit of self diffusion, i.”
• The Geometric Foundations of Hamiltonian Monte Carlo (2014, unknown): “One evident association is to molecular dynamics [25,31,49], which tackles expectations of chemical systems with natural Hamiltonian structures.”
• The Properties of Hydrogen and Helium Under Extreme Conditions (2011, unknown): “These system sizes are comparable to the one used in (Scandolo, 2003) and (Bonev et al., 2004a) with CPMD, and considerably larger than the one used in (Vorberger et al., 2007b) and (Holst et al., 2008) with BOMD.”
• Machine learning molecular dynamics for the simulation of infrared spectra (2017, unknown): “17 In AIMD, the motion of the nuclei is described classically according to Newton ’ s equations of motion 18 and depends on the quantum mechanical force exerted by the electrons and nuclei.”
• Acidity of edge surface sites of montmorillonite and kaolinite (2013, unknown): “…dynamics (FPMD) or density functional theory based molecular dynamics (DFTMD) has been playing more and more important roles in research on mineral interface systems (Boulet et al., 2006; Cygan et al., 2009; Marx and Hutter, 2009; Suter et al., 2009; Skelton et al., 2011; Tunega et al., 2011).”
• Gibbs2: A new version of the quasiharmonic model code. II. Models for solid-state thermodynamics, features and implementation (2011, unknown): “There are essentially two mainstream ways of incorporating temperature in a theoretical calculation: molecular dynamics simulations [5,6] and the quasiharmonic approximation (QHA) [7].”
• Emergence of magnetism in graphene materials and nanostructures (2010, unknown): “This approach is now implemented in a large number of public computer codes and well described in a variety of graduate-level textbooks (for instance, see (Koch and Holthausen, 2002; Martin, 2004; Marx and Hutter, 2009)).”
• Low-Depth Quantum Simulation of Materials (2017, unknown): “, surfaces (periodic in two dimensions), nanowires (periodic in one dimension), or even single molecules (periodic in zero dimensions) [127].”
• Representing potential energy surfaces by high-dimensional neural network potentials (2014, unknown): “In particular, for MD this has become a well-established technique and such ab initio MD [5, 6] simulations have been applied with great success to many interesting problems in chemistry and condensed matter physics.”
• Universal Transforming Geometric Network (2019, unknown): “Earlier computational methods for predicting protein structure includes molecular dynamics (MD), which use physic based equations to simulate the trajectory of a protein’s folding process into a stable final 3D conformation (Marx & Hutter (2010)).”